Statistical Computing


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Statistical Computing

  1. 1. Katalog książekanglojęzycznych STATYSTYKA OBLICZENIOWA Amerykańskie Towarzystwo Statystyczne ogłosiło rok 2013 Międzynarodowym Rokiem Statystyki. Ogólnoświatowa akcja ma na celu uczczenie i docenienie osiągnięć nauk statystycznych. Przyłączyliśmy się do akcji promując najważniejsze książki z tej dziedziny. Przeglądaj katalog z nowościami i najciekawszymi publikacjami.Dowiedz się więcej na
  2. 2.     Statystyka obliczeniowa A Visual Guide to Stata Graphics Advanced Markov Chain Monte Carlo Methods Michael N. Mitchell Faming Liang Taylor & Francis Wiley 9781597181068  9780470748268  07.02.2012 16.07.2010 Oprawa: miękka Oprawa: twarda £ 57,99 £ 67,50Whether you are new to Stata graphics or a seasoned veteran, A Visual Guide to Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool inStata Graphics, Third Edition will reach you how to use Stata to make publication- scientific computing. This book discusses recent developments of MCMC methodsquality graphics that will stand out and enhance your statistical results. With over with an emphasis on those making use of past sample information during900 illustrated examples and quick-reference tabs, this book quickly guides you to simulations. The application examples are drawn from diverse fields such asthe information you need for creating and customizing high-quality graphs for any bioinformatics, machine learning, social science, combinatorial optimization, andtype of statistical data. Each graph is displayed in full color with simple and clear computational physics. Key Features: Expanded coverage of the stochasticinstructions that illustrate how to create and customize graphs using either Stata approximation Monte Carlo and dynamic weighting algorithms that are essentiallycommands or the Stata Graph Editor. Statas powerful graphics system gives you immune to local trap problems. A detailed discussion of the Monte Carlo Metropolis-complete control over how the elements of your graph look, from marker symbols to Hastings algorithm that can be used for sampling from distributions with intractablelines, from legends to captions and titles, from axis labels to grid lines, and more. normalizing constants. Up-to-date accounts of recent developments of the GibbsWhether you use this book as a learning tool or a quick reference, you will have the sampler. Comprehensive overviews of the population-based MCMC algorithms andpower of Stata graphics at your fingertips. The third edition has been updated and the MCMC algorithms with adaptive proposals. This book can be used as aexpanded to reflect new Stat graphics features, and includes many additional textbook or a reference book for a one-semester graduate course in statistics,examples. This updated edition illustrates new features to specify fonts and computational biology, engineering, and computer sciences. Applied or theoreticalsymbols. researchers will also find this book beneficial. An Elementary Introduction to An Introduction to Stata for Health Statistical Learning Theory Researchers Sanjeev Kulkarni Svend Juul Wiley Taylor & Francis 9780470641835  9781597180771  15.07.2011 01.10.2010 Oprawa: twarda Oprawa: miękka £ 66,50 £ 49,99A thought-provoking look at statistical learning theory and its role in understanding An Introduction to Stata for Health Researchers, Third Edition systematically covershuman learning and inductive reasoning A joint endeavor from leading researchers in data management, simple description and analysis, and more advanced analysesthe fields of philosophy and electrical engineering, An Elementary Introduction to that are most often used in health research, such as regression models, survivalStatistical Learning Theory is a comprehensive and accessible primer on the rapidly analysis, measurement, and diagnosis. It also describes many graph types as wellevolving fields of statistical pattern recognition and statistical learning theory. as how to modify the appearance of a graph. Throughout the text, the authorsExplaining these areas at a level and in a way that is not often found in other books emphasize the importance of good documentation habits to prevent errors andon the topic, the authors present the basic theory behind contemporary machine wasted time. They demonstrate the use of strategies and tools for documentation.learning and uniquely utilize its foundations as a framework for philosophical thinking Robust data sets can be downloaded from the books website. Whats New Thisabout inductive inference. Promoting the fundamental goal of statistical learning, third edition presents some of the new features in Stata 11, including the new,knowing what is achievable and what is not, this book demonstrates the value of a flexible syntax for factor variables. It also incorporates Stata 11 in the rewrittensystematic methodology when used along with the needed techniques for chapters on regression and survival analysis. Taking into account the improvedevaluating the performance of a learning system. First, an introduction to machine availability of online documentation, this edition points to further reading in the onlinelearning is presented that includes brief discussions of applications such as image manuals.recognition, speech recognition, medical diagnostics, and statistical arbitrage. An R Companion to Linear Statistical Analysis of Questionnaire Data with R Models Christopher Hay-Jahans Bruno Falissard Taylor & Francis Taylor & Francis 9781439873656  9781439817667  20.10.2011 26.09.2011 Oprawa: twarda Oprawa: twarda £ 52,99 £ 59,99Focusing on user-developed programming, An R Companion to Linear Statistical While theoretical statistics relies primarily on mathematics and hypotheticalModels serves two audiences: those who are familiar with the theory and situations, statistical practice is a translation of a question formulated by aapplications of linear statistical models and wish to learn or enhance their skills in R; researcher into a series of variables linked by a statistical tool. As with writtenand those who are enrolled in an R-based course on regression and analysis of material, there are almost always differences between the meaning of the originalvariance. For those who have never used R, the book begins with a self-contained text and translated text. Additionally, many versions can be suggested, each withintroduction to R that lays the foundation for later chapters. their advantages and disadvantages. Analysis of Questionnaire Data with RThis book includes extensive and carefully explained examples of how to write translates certain classic research questions into statistical formulations. Asprograms using the R programming language. These examples cover methods used indicated in the title, the syntax of these statistical formulations is based on the well-for linear regression and designed experiments with up to two fixed-effects factors, known R language, chosen for its popularity, simplicity, and power of its structure.including blocking variables and covariates. It also demonstrates applications of Although syntax is vital, understanding the semantics is the real challenge of anyseveral pre-packaged functions for complex computational procedures. good translation. In this book, the semantics of theoretical-to-practical translation emerges progressively from examples and experience, and occasionally from mathematical considerations. Sometimes the interpretation of a result is not clear, and there is no statistical tool really suited to the question at hand. Sometimes data sets contain errors, inconsistencies between answers, or missing data. 2
  3. 3.   Statystyka obliczeniowa Applied Medical Statistics Using SAS Applied Reliability Geoff Der David C. Trindade Taylor & Francis Taylor & Francis 9781439867976  9781584884668  30.10.2012 26.08.2011 Oprawa: twarda Oprawa: twarda £ 57,99 £ 63,99Written with medical statisticians and medical researchers in mind, this intermediate- Since the publication of the second edition of Applied Reliability in 1995, the readylevel reference explores the use of SAS for analyzing medical data. Applied Medical availability of inexpensive, powerful statistical software has changed the wayStatistics Using SAS covers the whole range of modern statistical methods used in statisticians and engineers look at and analyze all kinds of data. Problems inthe analysis of medical data, including regression, analysis of variance and reliability that were once difficult and time consuming even for experts can now becovariance, longitudinal and survival data analysis, missing data, generalized additive solved with a few well-chosen clicks of a mouse. However, software documentationmodels (GAMs), and Bayesian methods. The book focuses on performing these has had difficulty keeping up with the enhanced functionality added to new releases,analyses using SAS, the software package of choice for those analysing medical especially in specialized areas such as reliability analysis. Using analysis capabilitiesdata. in spreadsheet software and two well-maintained, supported, and frequentlyFeatures updated, popular software packages-Minitab and SAS JMP-the third edition of Covers the planning stage of medical studies in detail; several chapters contain Applied Reliability is properties of lifetime distributions such as the exponential, concepts, and the an easy-to-use guide to basic descriptive statistics, reliability details of sample size estimation Weibull, and lognormal. The material covers reliability data plotting, acceleration Illustrates methods of randomisation that might be employed for clinical trials models, life test data analysis, systems models, and much more. The third edition Covers topics that have become of great importance in the 21st century, includes a new chapter on Bayesian reliability analysis and expanded, updated including Bayesian methods and multiple imputation coverage of repairable system modeling. Applied Survey Data Analysis Bayesian Computation with R Steven G. Heeringa Jim Albert Taylor & Francis Springer 9781420080667  9780387922973  07.04.2010 01.06.2009 Oprawa: twarda Oprawa: miękka £ 52,99 € 44,95Taking a practical approach that draws on the authors’ extensive teaching, There has been a dramatic growth in the development and application of Bayesianconsulting, and research experiences, Applied Survey Data Analysis provides an inferential methods. Some of this growth is due to the availability of powerfulintermediate-level statistical overview of the analysis of complex sample survey data. simulation-based algorithms to summarize posterior distributions. There has beenIt emphasizes methods and worked examples using available software procedures also a growing interest in the use of the system R for statistical analyses. Rs openwhile reinforcing the principles and theory that underlie those methods. source nature, free availability, and large number of contributor packages haveAfter introducing a step-by-step process for approaching a survey analysis problem, made R the software of choice for many statisticians in education and industry.the book presents the fundamental features of complex sample designs and shows Bayesian Computation with R introduces Bayesian modeling by the use ofhow to integrate design characteristics into the statistical methods and software for computation using the R language. The early chapters present the basic tenets ofsurvey estimation and inference. The authors then focus on the methods and Bayesian thinking by use of familiar one and two-parameter inferential problems.models used in analyzing continuous, categorical, and count-dependent variables; Bayesian computational methods such as Laplaces method, rejection sampling,event history; and missing data problems. Some of the techniques discussed and the SIR algorithm are illustrated in the context of a random effects model. Theinclude univariate descriptive and simple bivariate analyses, the linear regression construction and implementation of Markov Chain Monte Carlo (MCMC) methods ismodel, generalized linear regression modeling methods, the Cox proportional introduced. These simulation-based algorithms are implemented for a variety ofhazards model, discrete time models, and the multiple imputation analysis method. Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Bayesian Programming Computational and Statistical Methods for Protein Quantification by Mass Spectrometry Pierre Bessiere Ingvar Eidhammer Taylor & Francis Wiley 9781439880326  9781119964001  06.10.2013 04.01.2013 Oprawa: twarda Oprawa: twarda £ 57,99 £ 60,00To create a complete computing Bayesian framework, a new modeling The definitive introduction to data analysis in quantitative proteomics This bookmethodology is needed to build probabilistic models; new inference algorithms are provides all the necessary knowledge about mass spectrometry based proteomicsrequired to automate probabilistic calculus; and new programming languages are methods and computational and statistical approaches to pursue the planning,sought to implement these models on computers; eventually, new hardware will also design and analysis of quantitative proteomics experiments. The authors carefullybe necessary to run these Bayesian programs efficiently. This book describes the constructed approach allows readers to easily make the transition into the field ofcurrent first steps toward the ultimate goal of creating a Bayesian computer. Written quantitative proteomics. Through detailed descriptions of wet-lab methods,by leading researchers, the text focuses on Bayesian programming and requires computational approaches and statistical tools, this book covers the full scope of aonly basic knowledge of mathematics, making it accessible to non-experts. It quantitative experiment, allowing readers to acquire new knowledge as well aspresents applications from various areas such as robotics. acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises. Provides both clear and rigorous descriptions of methods and 3
  4. 4.     Statystyka obliczeniowa Computational Statistics Data Analysis and Graphics Using R: An Example-Based Approach Geof H. Givens John Maindonald Wiley Cambridge University Press 9780470533314  9780521762939  07.12.2012 06.05.2010 Oprawa: twarda Oprawa: twarda £ 83,50 £ 50,00This new edition continues to serve as a comprehensive guide to modern and Discover what you can do with R! Introducing the R system, covering standardclassical methods of statistical computing. The book is comprised of four main regression methods, then tackling more advanced topics, this book guides usersparts spanning the field: through the practical, powerful tools that the R system provides. The emphasis is on Optimization hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is Integration and Simulation done and why. The companion website has code and datasets, allowing readers to Bootstrapping reproduce all analyses, along with solutions to selected exercises and updates. Density Estimation and Smoothing Assuming basic statistical knowledge and some experience with data analysis (butWithin these sections,each chapter includes a comprehensive introduction and step not R), the book is ideal for research scientists, final-year undergraduate or graduate-by-step implementation summaries to accompany the explanations of key -level students of applied statistics, and practising statisticians. It is both for learningmethods. The new edition includes updated coverage and existing topics as well as and for reference. This third edition expands upon topics such as Bayesiannew topics such as adaptive MCMC and bootstrapping for correlated data. The inference for regression, errors in variables, generalized linear mixed models, andbook website now includes comprehensive R code for the entire book. There are random forests.extensive exercises, real examples, and helpful insights about how to use themethods in practice. Data Management Using Stata: A Data Manipulation with R Practical Handbook Michael N. Mitchell Phil Spector Taylor & Francis Springer 9781597180764  9780387747309  08.07.2010 07.04.2008 Oprawa: miękka Oprawa: miękka £ 44,99 € 59,95Using simple language and illustrative examples, this book comprehensively covers This book presents a wide array of methods applicable for reading data into R, anddata management tasks that bridge the gap between raw data and statistical efficiently manipulating that data. In addition to the built-in functions, a number ofanalysis. Rather than focus on clusters of commands, the author takes a modular readily available packages from CRAN (the Comprehensive R Archive Network) areapproach that enables readers to quickly identify and implement the necessary task also covered. All of the methods presented take advantage of the core features ofwithout having to access background information first. Each section in the chapters R: vectorization, efficient use of subscripting, and the proper use of the variedpresents a self-contained lesson that illustrates a particular data management task functions in R that are provided for common data management tasks. Mostvia examples, such as creating data variables and automating error checking. The experienced R users discover that, especially when working with large data sets, ittext also discusses common pitfalls and how to avoid them and provides strategic may be helpful to use other programs, notably databases, in conjunction with management advice. Ideal for both beginning statisticians and experienced Accordingly, the use of databases in R is covered in detail, along with methods forusers, this handy book helps readers solve problems and learn comprehensive data extracting data from spreadsheets and datasets created by other skills. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Data Mining with Rattle and R Dynamic Prediction in Clinical Survival Analysis Graham Williams Hans van Houwelingen Springer Taylor & Francis 9781441998897  9781439835333  25.02.2011 10.11.2011 Oprawa: miękka Oprawa: twarda € 54,95 £ 59,99Data mining is the art and science of intelligent data analysis. By building knowledge There is a huge amount of literature on statistical models for the prediction offrom information, data mining adds considerable value to the ever increasing stores survival after diagnosis of a wide range of diseases like cancer, cardiovascularof electronic data that abound today. In performing data mining many decisions disease, and chronic kidney disease. Current practice is to use prediction modelsneed to be made regarding the choice of methodology, the choice of data, the based on the Cox proportional hazards model and to present those as static modelschoice of tools, and the choice of algorithms. Throughout this book the reader is for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction inintroduced to the basic concepts and some of the more popular algorithms of data Clinical Survival Analysis focuses on dynamic models for the remaining lifetime atmining. With a focus on the hands-on end-to-end process for data mining, Williams later points in time, for instance using landmark models. Designed to be useful toguides the reader through various capabilities of the easy to use, free, and open applied statisticians and clinical epidemiologists, each chapter in the book has asource Rattle Data Mining Software built on the sophisticated R Statistical Software. practical focus on the issues of working with real life data. Chapters conclude withThe focus on doing data mining rather than just reading about data mining is additional material either on the interpretation of the models, alternative models, orrefreshing. This book covers data understanding, data preparation, data refinement, theoretical background.model building, model evaluation, and practical deployment. The reader will learn torapidly deliver a data mining project using software easily installed for free from theInternet. "Coupling Rattle with R" delivers a very sophisticated data miningenvironment with all the power, and more, of the many commercial offerings. 4
  5. 5.   Statystyka obliczeniowa Equivalence and Noninferiority Tests for Flexible Parametric Survival Analysis Quality, Manufacturing and Test Engineers Using Stata Scott Pardo Patrick Royston Taylor & Francis Taylor & Francis 9781466586888  9781597180795  15.09.2013 15.08.2011 Oprawa: twarda Oprawa: miękka £ 82,00 £ 49,99This work provides readers with a set of statistical methods useful for demonstrating Through real-world case studies, this book shows how to use Stata to estimate aequivalence or noninferiority of new or revised systems either compared to class of flexible parametric survival models. It discusses the modeling of time-previously existing systems or previously established performance guidelines. These dependent and continuous covariates and looks at how relative survival can be usedtests are often performed in the context of process or product validation. The author to measure mortality associated with a particular disease when the cause of deathdescribes all the necessary calculations, which can be made using software such as has not been recorded. The book describes simple quantification of differencesMinitab or JMP. Fully worked examples are provided for each method. between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences. Foundations of Interconnection Ggplot2 Networks A.Yavuz Oruc Hadley Wickham Taylor & Francis Springer 9781466559424  9780387981406  05.12.2013 07.08.2009 Oprawa: twarda Oprawa: miękka £ 57,99 € 52,95Suitable for researchers and graduate students in computer science, electrical This book describes ggplot2, a new data visualization package for R that uses theengineering, and applied mathematics, this book presents a concise yet complete insights from Leland Wilkisons Grammar of Graphics to create a powerful andaccount of the most significant results in interconnection network theory. The flexible system for creating data graphics. With ggplot2, its easy to:authors give a mathematical classification and characterization of the keyinterconnection functions of networks and establish their switching factor  produce handsome, publication-quality plots, with automatic legends created from the plot specificationcomplexities using combinatorial methods. The text leads readers through thehistorical development, covering major contributions from Shannon, Clos, Benes,  superpose multiple layers (points, lines, maps, tiles, box plots to name a few)Cantor, Pinsker, and Margulis. It also explores recent results, such as quantum from different data sources, with automatically adjusted common scalesswitching networks.  add customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regression  save any ggplot2 plot (or part thereof) for later modification or reuse  create custom themes that capture in-house or journal style requirements, and that can easily be applied to multiple plots Guidebook to R Graphics Using Handbook of Partial Least Squares: Microsoft Windows Concepts, Methods and Applications in Kunio Takezawa Vincenzo Esposito Vinzi Wiley Springer 9781118026397  9783540328254  30.03.2012 30.05.2007 Oprawa: miękka Oprawa: twarda £ 46,95 € 286This book introduces the graphical capabilities of R to readers new to the software, This handbook provides a comprehensive overview of Partial Least Squares (PLS)taking readers step by step through the process of creating histograms, boxplots, methods with specific reference to their use in marketing and with a discussion ofstrip charts, time series graphs, steam-and-leaf displays, scatterplot matrices, and the directions of current research and perspectives. It covers the broad area of PLSmap graphs. Throughout the book, concise explanations of key concepts of R methods, from regression to structural equation modeling applications, software andgraphics assist readers in carrying out the presented procedures. The discussed interpretation of results. The handbook serves both as an introduction for thosetechniques are accompanied by a wealth of screenshots and graphics with related without prior knowledge of PLS and as a comprehensive reference for researchersR code available on the books FTP site, and numerous exercises allow readers to and practitioners interested in the most recent advances in PLS methodology.test their understanding of the presented 5
  6. 6.     Statystyka obliczeniowa Handbook of SAS Data Step Image Statistics and Computer Programming Graphics Arthur Li Tania Pouli Taylor & Francis Taylor & Francis 9781466552388  9781568817255  10.05.2013 06.11.2013 Oprawa: twarda Oprawa: twarda £ 38,99 £ 44,99To write an accomplished program in the DATA step of SAS®, programmers must The statistics of natural images have attracted the attention of researchers in aunderstand programming logic and know how to implement and even create their variety of fields as a means to better understand the human visual system and itsown programming algorithm. Handbook of SAS® DATA Step Programming shows processes. A number of algorithms in computer graphics and vision and imagereaders how best to manage and manipulate data by using the DATA step. processing take advantage of such statistical findings to create visually moreThe book helps novices avoid common mistakes resulting from a lack of plausible results. This book explores the state of the art in image statistics andunderstanding fundamental and unique SAS programming concepts. It explains that discusses existing and potential applications within computer graphics and relatedlearning syntax does not solve all problems; rather, a thorough comprehension of areas.SAS processing is needed for successful programming. The author also guidesreaders through a programming task. In most of the examples, the author firstpresents strategies and steps for solving the problem, then offers a solution, andfinally gives a more detailed explanation of the solution.Understanding the DATA steps, particularly the program data vector (PDV), is criticalto proper data manipulation and management in SAS. This book helps SASprogrammers thoroughly grasp the concept of DATA step processing and writeaccurate programs in the DATA step. Industrial Statistics with Minitab Introducing Monte Carlo Methods with R Pere Grima Cintas Christian P. Robert (INSEE, Malakoff, France) Wiley Springer 9780470972755  9781441915757  14.09.2012 07.12.2009 Oprawa: twarda Oprawa: miękka £ 55,00 € 54,95Industrial Statistics with MINITAB demonstrates the use of MINITAB as a tool for Computational techniques based on simulation have now become an essential partperforming statistical analysis in an industrial context. This book covers introductory of the statisticians toolbox. It is thus crucial to provide statisticians with a practicalindustrial statistics, exploring the most commonly used techniques alongside those understanding of those methods, and there is no better way to develop intuition andthat serve to give an overview of more complex issues. A plethora of examples in skills for simulation than to use simulation to solve statistical problems. IntroducingMINITAB are featured along with case studies for each of the statistical techniques Monte Carlo Methods with R covers the main tools used in statistical simulationpresented. Industrial Statistics with MINITAB : Provides comprehensive coverage of from a programmers point of view, explaining the R implementation of eachuser-friendly practical guidance to the essential statistical methods applied in simulation technique and providing the output for better understanding andindustry. Explores statistical techniques and how they can be used effectively with comparison. While this book constitutes a comprehensive treatment of simulationthe help of MINITAB 16. Contains extensive illustrative examples and case studies methods, the theoretical justification of those methods has been considerablythroughout and assumes no previous statistical knowledge. Emphasises data reduced, compared with Robert and Casella (2004). Similarly, the more exploratorygraphics and visualization, and the most used industrial statistical tools, such as and less stable solutions are not covered here. This book does not require aStatistical Process Control and Design of Experiments. Is supported by an preliminary exposure to the R programming language or to Monte Carlo methods,accompanying website featuring case studies and the corresponding datasets. Six nor an advanced mathematical background. While many examples are set within aSigma Green Belts and Black Belts will find explanations and examples of the most Bayesian framework, advanced expertise in Bayesian statistics is not required.relevant techniques in DMAIC projects. Introduction to Digital Systems: Large-Scale Inverse Problems and Modeling, Synthesis, and Simulation Quantification of Uncertainty Mohammed Ferdjallah Lorenz T. Biegler Wiley Wiley 9780470900550  9780470697436  22.07.2011 05.11.2010 Oprawa: twarda Oprawa: twarda £ 76,95 £ 79,95A unique guide to using both modeling and simulation in digital systems design This book focuses on computational methods for large-scale statistical inverseDigital systems design requires rigorous modeling and simulation analysis that problems and provides an introduction to statistical Bayesian and frequentisteliminates design risks and potential harm to users. Introduction to Digital Systems: methodologies. Recent research advances for approximation methods areModeling, Synthesis, and Simulation Using VHDL introduces the application of discussed, along with Kalman filtering methods and optimization-based approachesmodeling and synthesis in the effective design of digital systems and explains to solving inverse problems. The aim is to cross-fertilize the perspectives ofapplicable analytical and computational methods. Through step-by-step researchers in the areas of data assimilation, statistics, large-scale optimization,explanations and numerous examples, the author equips readers with the tools applied and computational mathematics, high performance computing, and cutting-needed to model, synthesize, and simulate digital principles using Very High Speed edge applications. The solution to large-scale inverse problems critically depends onIntegrated Circuit Hardware Description Language (VHDL) programming. methods to reduce computational cost. Recent research approaches tackle thisExtensively classroom-tested to ensure a fluid presentation, this book provides a challenge in a variety of different ways. Many of the computational frameworkscomprehensive overview of the topic by integrating theoretical principles, discrete highlighted in this book build upon state-of-the-art methods for simulation of themathematical models, computer simulations, and basic methods of analysis. forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced- order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other ... 6
  7. 7.   Statystyka obliczeniowa Making Sense of Data Set Maximum Likelihood Estimation with Stata Glenn J. Myatt William Gould Wiley Taylor & Francis 9781118395141  9781597180788  30.03.2012 15.11.2010 Oprawa: miękka Oprawa: miękka £ 153,00 £ 49,99 Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands. Methods of Statistical Model Estimation Modeling and Simulation Fundamentals: Theoretical Joseph M. Hilbe John A. Sokolowski Taylor & Francis Wiley 9781439858028  9780470486740  08.07.2013 14.05.2010 Oprawa: twarda Oprawa: twarda £ 57,99 £ 76,50Methods of Statistical Model Estimation provides readers with an examination of the An insightful presentation of the key concepts, paradigms, and applications ofmajor methods used by researchers and programmers to estimate statistical model modeling and simulation Modeling and simulation has become an integral part ofparameters and associated statistics. Designed for R programmers, the book is also research and development across many fields of study, having evolved from a toolsuitable for anyone wanting to better understand the optimization algorithms used to a discipline in less than two decades. Modeling and Simulation Fundamentalsfor model estimation. The text focuses on R programming code for the estimation of offers a comprehensive and authoritative treatment of the topic and includesa variety of regression procedures using maximum likelihood estimation, iteratively definitions, paradigms, and applications to equip readers with the skills needed toreweighted least squares regression, the EM algorithm, and MCMC sampling. Fully work successfully as developers and users of modeling and simulation. Featuringdeveloped code is constructed in the book for each of the discussed methods of contributions written by leading experts in the field, the books fluid presentationestimation, including working code for OLS regression, a near complete generalized builds from topic to topic and provides the foundation and theoretical underpinningslinear models function, one- and two-parameter maximum likelihood models for of modeling and simulation. First, an introduction to the topic is presented, includingboth pooled and panel models, a random effects model estimated using the EM related terminology, examples of model development, and various domains ofalgorithm, and a Bayesian Poisson model using Metropolis-Hastings sampling. The modeling and simulation.authors also discuss a number of ancillary issues. Modern Signal Processing Multivariate Survival and Competing Risks Daniel N. Rockmore Martin J. Crowder Cambridge University Press Taylor & Francis 9780521158213  9781439875216  15.07.2010 17.05.2012 Oprawa: miękka Oprawa: twarda £ 32,99 £ 63,99Signal processing is everywhere in modern technology. Its mathematical basis and Multivariate Survival Analysis and Competing Risks introduces univariate survivalmany areas of application are the subject of this 2004 book, based on a series of analysis and extends it to the multivariate case. It covers competing risks andgraduate-level lectures held at the Mathematical Sciences Research Institute. counting processes and provides many real-world examples, exercises, and REmphasis is on challenges in the subject, particular techniques adapted to particular code. The text discusses survival data, survival distributions, frailty models,technologies, and certain advances in algorithms and theory. The book covers two parametric methods, multivariate data and distributions, copulas, continuous failure,main areas: computational harmonic analysis, envisioned as a technology for parametric likelihood inference, and non- and semi-parametric methods. There areefficiently analysing real data using inherent symmetries; and the challenges inherent many books covering survival analysis, but very few that cover the multivariate casein the acquisition, processing and analysis of images and sensing data in general in any depth. Written for a graduate-level audience in statistics/biostatistics, this[EMDASH] ranging from sonar on a submarine to a neuroscientists fMRI study. book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of 7
  8. 8.     Statystyka obliczeniowa Network and Adaptive Sampling Numerical Methods of Statistics Techniques Arijit Chaudhuri John F. Monahan Taylor & Francis Cambridge University Press 9781466577565  9780521139519  22.06.2013 18.04.2011 Oprawa: twarda Oprawa: miękka £ 49,99 £ 36,99Combining the two statistical techniques of network sampling and adaptive This book explains how computer software is designed to perform the taskssampling, this book illustrates the advantages of using them in tandem to effectively required for sophisticated statistical analysis. For statisticians, it examines the nitty-capture sparsely located elements in unknown pockets. It shows how network gritty computational problems behind statistical methods. For mathematicians andsampling is a reliable guide in capturing inaccessible entities through linked computer scientists, it looks at the application of mathematical tools to statisticalauxiliaries. The text also explores how adaptive sampling is strengthened in problems. The first half of the book offers a basic background in numerical analysisinformation content through subsidiary sampling with devices to mitigate that emphasizes issues important to statisticians. The next several chapters cover aunmanageable expanding sample sizes. Empirical data illustrates the applicability of broad array of statistical tools, such as maximum likelihood and nonlinearboth methods. regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the authors website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder-Mead search algorithm. Practical Statistical Methods: A SAS Probability and Statistics for Computer Programming Approach Scientists Lakshmi Padgett Michael Baron Taylor & Francis Taylor & Francis 9781439812822  9781439875902  25.04.2011 15.08.2013 Oprawa: twarda Oprawa: twarda £ 52,99 £ 63,99Practical Statistical Methods: A SAS Programming Approach presents a broad Presenting probability and statistical methods, simulation techniques, and modelingspectrum of statistical methods useful for researchers without an extensive tools, this book helps students solve problems and make optimal decisions instatistical background. In addition to nonparametric methods, it covers methods for uncertain conditions, select stochastic models, compute probabilities and forecasts,discrete and continuous data. Omitting mathematical details and complicated and evaluate performance of computer systems and networks. It covers how toformulae, the text provides SAS programs to carry out the necessary analyses and read a word problem or a corporate report, realize the uncertainty involved in thedraw appropriate inferences for common statistical problems. After introducing described situation, select a suitable probability model, estimate and test itsfundamental statistical concepts, the author describes methods used for parameters based on real data, compute probabilities, and make appropriatequantitative data and continuous data following normal and nonnormal distributions. conclusions. This edition features over 100 pages of new material coveringShe then focuses on regression methodology, highlighting simple linear regression, categorical data analysis, nonparametric tests, and regression diagnostics.logistic regression, and the proportional hazards model. The final chapter brieflydiscusses such miscellaneous topics as propensity scores, misclassification errors,interim analysis, conditional power, bootstrap, and jackknife. With SAS code andoutput integrated throughout, this book shows how to interpret data using SAS andillustrates the many statistical methods available for tackling problems in a range offields, including the pharmaceutical industry and the social sciences. Quasi-Least Squares Regression R for SAS and SPSS Users Justine Shults Robert A. Muenchen Taylor & Francis Springer 9781420099935  9781461406846  06.12.2013 23.07.2011 Oprawa: twarda Oprawa: twarda £ 57,99 € 89,95Drawing on the authors’ substantial expertise in modeling longitudinal and clustered R is a powerful and free software system for data analysis and graphics, with overdata, this book presents a comprehensive treatment of quasi-least squares (QLS) 1,200 add-on packages available. This book introduces R using SAS and SPSSregression—a computational approach for the estimation of correlation parameters terms with which you are already familiar. It demonstrates which of the add-onwithin the framework of generalized estimating equations (GEEs). The authors packages are most like SAS and SPSS and compares them to Rs built-in functions.present an overview and detailed evaluation of QLS methodology, demonstrating It steps through over 30 programs written in all three packages, comparing andthe advantages of QLS in comparison with alternative methods. They describe how contrasting the packages differing approaches. The programs and practiceQLS can be used to extend the application of the traditional GEE approach to the datasets are available for download. The glossary defines over 50 R terms usinganalysis of unequally spaced longitudinal data, familial data, and data with multiple SAS/SPSS jargon and again using R jargon. The table of contents and the indexsources of correlation. In some settings, QLS also allows for improved analysis with allow you to find equivalent R functions by looking up both SAS statements andan unstructured correlation matrix. SPSS commands. When finished, you will be able to import data, manage andSpecial focus is given to goodness-of-fit analysis as well as new strategies for transform it, create publication quality graphics, and perform basic statisticalselecting the appropriate working correlation structure for QLS and GEE. analyses. This new edition has updated programming, an expanded index, and even more statistical methods covered in over 25 new sections. 8
  9. 9.   Statystyka komputerowa R for Statistics R Graphics Pierre-Andre Cornillon Paul Murrell Taylor & Francis Taylor & Francis 9781439881453  9781439831762  17.04.2012 07.06.2011 Oprawa: miękka Oprawa: twarda £ 34,99 £ 52,99Although there are currently a wide variety of software packages suitable for the Extensively updated to reflect the evolution of statistics and computing, the secondmodern statistician, R has the triple advantage of being comprehensive, edition of the bestselling R Graphics comes complete with new packages and newwidespread, and free. Published in 2008, the second edition of Statistiques avec R examples. Paul Murrell, widely known as the leading expert on R graphics, hasenjoyed great success as an R guidebook in the French-speaking world. Translated developed an in-depth resource that helps both neophyte and seasoned usersand updated, R for Statistics includes a number of expanded and additional worked master the intricacies of R graphics. New in the Second Edition Updated informationexamples. Organized into two sections, the book focuses first on the R software, on the core graphics engine, the traditional graphics system, the grid graphicsthen on the implementation of traditional statistical methods with R. Focusing on the system, and the lattice package A new chapter on the ggplot2 package NewR software, the first section covers: Basic elements of the R software and data chapters on applications and extensions of R Graphics, including geographic maps,processing Clear, concise visualization of results, using simple and complex graphs dynamic and interactive graphics, and node-and-edge graphs Organized into fiveProgramming basics: pre-defined and user-created functions The second section of parts, R Graphics covers both "traditional" and newer, R-specific graphics systems.the book presents R methods for a wide range of traditional statistical data The book reviews the graphics facilities of the R language and describes Rsprocessing techniques, including: Regression methods Analyses of variance and powerful grid graphics system. It then covers the graphics engine, which representscovariance Classification methods Exploratory multivariate analysis Clustering a common set of fundamental graphics facilities, and provides a series of briefmethods Hypothesis tests After a short presentation of the method, the book overviews of the major areas of application for R graphics and the major extensionsexplicitly details the R command lines and gives commented results. of R graphics. Solving Differential Equations in R Spatial Analysis Along Networks Karline Soetaert Atsuyuki Okabe Springer Wiley 9783642280696  9780470770818  07.06.2012 27.07.2012 Oprawa: miękka Oprawa: twarda € 49,95 £ 65,00Mathematics plays an important role in many scientific and engineering disciplines. In the real world, there are numerous and various events that occur on andThis book deals with the numerical solution of differential equations, a very important alongside networks, including the occurrence of traffic accidents on highways, thebranch of mathematics. Our aim is to give a practical and theoretical account of how location of stores alongside roads, the incidence of crime on streets and theto solve a large variety of differential equations, comprising ordinary differential contamination along rivers. In order to carry out analyses of those events, theequations, initial value problems and boundary value problems, differential algebraic researcher needs to be familiar with a range of specific techniques. Spatial Analysisequations, partial differential equations and delay differential equations. The solution Along Networks provides a practical guide to the necessary statistical techniquesof differential equations using R is the main focus of this book. It is therefore and their computational implementation. Each chapter illustrates a specificintended for the practitioner, the student and the scientist, who wants to know how technique, from Stochastic Point Processes on a Network and Network Voronoito use R for solving differential equations. However, it has been our goal that non- Diagrams, to Network K-function and Point Density Estimation Methods, and themathematicians should at least understand the basics of the methods, while Network Huff Model. The authors also discuss and illustrate the undertaking of theobtaining entrance into the relevant literature that provides more mathematical statistical tests described in a Geographical Information System (GIS) environmentbackground. Therefore, each chapter that deals with R examples is preceded by a as well as demonstrating the user-friendly free software package SANET.chapter where the theory behind the numerical methods being used is introduced. Statistical Data Mining Using SAS Statistical Inference Applications George Fernandez Murray Aitkin Taylor & Francis Taylor & Francis 9781439810750  9781420093438  29.06.2010 01.06.2010 Oprawa: twarda Oprawa: twarda £ 62,99 £ 62,99Statistical Data Mining Using SAS Applications, Second Edition describes statistical This book sets out an integrated approach to statistical inference using thedata mining concepts and demonstrates the features of user-friendly data mining likelihood function as the primary measure of evidence for statistical modelSAS tools. Integrating the statistical and graphical analysis tools available in SAS parameters, and for the statistical models themselves. The author provides both ansystems, the book provides complete statistical data mining solutions without alternative to standard Bayesian inference and the foundation for a course sequencewriting SAS program codes or using the point-and-click approach. Each chapter in modern Bayesian theory at the graduate or advanced undergraduate level. Theemphasizes step-by-step instructions for using SAS macros and interpreting the restriction of the book to evidence is deliberate: there are already many books onresults. Compiled data mining SAS macro files are available for download on the Bayesian and non-Bayesian decision theory, and the purpose of this one is lessauthors website. By following the step-by-step instructions and downloading the ambitious, but perhaps more relevant scientifically, in providing a detailedSAS macros, analysts can perform complete data mining analysis fast and prescription for the assessment of statistical evidence.effectively. New to the Second Edition-General Features Access to SAS macrosdirectly from desktop Compatible with SAS version 9, SAS Enterprise Guide, andSAS Learning Edition Reorganization of all help files to an appendix Ability to createpublication quality graphics Macro-call error check New Features in These SAS-Specific Macro Applications Converting PC data files to SAS data (EXLSAS2 macro)Randomly splitting data (RANSPLIT2) Frequency analysis (FREQ2) Univariate 9
  10. 10.     Statystyka komputerowa Statistical Inference: The Minimum Statistical Learning and Data Science Distance Approach Ayanendranath Basu Mireille Gettler Summa Taylor & Francis Taylor & Francis 9781420099652  9781439867631  16.06.2011 18.01.2012 Oprawa: twarda Oprawa: twarda £ 59,99 £ 59,99This book gives a comprehensive account of density-based minimum distance Data analysis is changing fast. Driven by a vast range of application domains andmethods and their use in statistical inference. It covers statistical distances, density- affordable tools, machine learning has become mainstream. Unsupervised databased minimum distance methods, discrete and continuous models, asymptotic analysis, including cluster analysis, factor analysis, and low dimensionality mappingdistributions, robustness, computational issues, residual adjustment functions, methods continually being updated, have reached new heights of achievement ingraphical descriptions of robustness, penalized and combined distances, the incredibly rich data world that we inhabit. Statistical Learning and Data Sciencemultisample methods, weighted likelihood, and multinomial goodness-of-it tests. is a work of reference in the rapidly evolving context of converging methodologies. ItThe book also introduces the minimum distance methodology in interdisciplinary gathers contributions from some of the foundational thinkers in the different fields ofareas, such as neural networks and image processing, as well as specialized data analysis to the major theoretical results in the domain. On the methodologicalmodels and problems, including regression, mixture models, survival and Bayesian front, the volume includes conformal prediction and frameworks for assessinganalysis, and more. confidence in outputs, together with attendant risk. It illustrates a wide range of applications, including semantics, credit risk, energy production, genomics, and ecology. The book also addresses issues of origin and evolutions in the unsupervised data analysis arena, and presents some approaches for time series, symbolic data, and functional data. The R Book The R Primer Michael J. Crawley Claus Thorn Ekstrom Wiley Taylor & Francis 9780470973929  9781439862063  07.12.2012 25.08.2011 Oprawa: twarda Oprawa: miękka £ 60,00 £ 26,99Hugely successful and popular text presenting an extensive and comprehensive Newcomers to R are often intimidated by the command-line interface, the vastguide for all R users The R language is recognized as one of the most powerful and number of functions and packages, or the processes of importing data andflexible statistical software packages, enabling users to apply many statistical performing a simple statistical analysis. The R Primer provides a collection oftechniques that would be impossible without such software to help implement such concise examples and solutions to R problems frequently encountered by newlarge data sets. R has become an essential tool for understanding and carrying out users of this statistical software. Rather than explore the many options available forresearch. This edition: Features full colour text and extensive graphics throughout. every command as well as the ever-increasing number of packages, the bookIntroduces a clear structure with numbered section headings to help readers locate focuses on the basics of data preparation and analysis and gives examples that caninformation more efficiently. Looks at the evolution of R over the past five years. be used as a starting point. The numerous examples illustrate a specific situation,Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully topic, or problem, including data importing, data management, classical statisticalrevised and updated bibliography and reference section. Is supported by an analyses, and high-quality graphics production. Each example is self-contained andaccompanying website allowing examples from the text to be run by the user. Praise includes R code that can be run exactly as shown, enabling results from the book tofor the first edition: ...if you are an R user or wannabe R user, this text is the one be replicated. While base R is used throughout, other functions or packages arethat should be on your shelf. listed if they cover or extend the functionality. After working through the examples found in this text, new users of R will be able to better handle data analysis and graphics applications in R. The R Student Companion Transforms and Applications Primer for Engineers with Examples and MATLAB Brian Dennis Alexander D. Poularikas Taylor & Francis Taylor & Francis 9781439875407  9781420089318  10.10.2012 09.03.2010 Oprawa: miękka Oprawa: twarda £ 25,99 £ 48,99R is the amazing, free, open-access software package for scientific graphs and Transforms and Applications Primer for Engineers with Examples and MATLABcalculations used by scientists worldwide. The R Student Companion is a student- [registered] is required reading for engineering and science students, professionals,oriented manual describing how to use R in high school and college science and and anyone working on problems involving transforms. This invaluable primermathematics courses. Written for beginners in scientific computation, the book contains the most essential integral transforms that both practicing engineers andassumes the reader has just some high school algebra and has no computer students need to understand. It provides a large number of examples to explain theprogramming background. The author presents applications drawn from all sciences use of transforms in different areas, including circuit analysis, differential equations,and social sciences and includes the most often used features of R in an appendix. signals and systems, and mechanical vibrations. It includes an appendix withIn addition, each chapter provides a set of computational challenges: exercises in R suggestions and explanations to help you optimize your use of MATLAB Laplacecalculations that are designed to be performed alone or in groups. Several of the and Fourier transforms are by far the most widely used and most useful of all integralchapters explore algebra concepts that are highly useful in scientific applications, transforms, so they are given a more extensive treatment in this book, compared tosuch as quadratic equations, systems of linear equations, trigonometric functions, other texts that include them. Offering numerous MATLAB functions created by theand exponential functions. Each chapter provides an instructional review of the author, this comprehensive book contains several appendices to complement thealgebra concept, followed by a hands-on guide to performing calculations and main subjects. Perhaps the most important feature is the extensive tables ofgraphing in R. R is intuitive, even fun. Fantastic, publication-quality graphs of data, transforms, which are provided to supplement the learning process.equations, or both can be produced with little effort. 10
  11. 11.   Statystyka komputerowa Tree-Based Graph Partitioning Understanding Biplots: Methods and Constraint Applications of Biplots Xavier Lorca John C. Gower Wiley Wiley 9781848213036  9780470012550  12.07.2011 24.12.2010 Oprawa: twarda Oprawa: twarda £ 63,50 £ 68,95Combinatorial problems based on graph partitioning enable us to mathematically Biplots are a graphical method for simultaneously displaying two kinds ofrepresent and model many practical applications. Mission planning and the routing information; typically, the variables and sample units described by a multivariate dataproblems occurring in logistics perfectly illustrate two such examples. Nevertheless, matrix or the items labelling the rows and columns of a two-way table. This bookthese problems are not based on the same partitioning pattern: generally, patterns aims to popularize what is now seen to be a useful and reliable method for thelike cycles, paths, or trees are distinguished. Moreover, the practical applications are visualization of multidimensional data associated with, for example, principaloften not limited to theoretical problems like the Hamiltonian path problem, or K- component analysis, canonical variate analysis, multidimensional scaling,node disjoint path problems. Indeed, they usually combine the graph partitioning multiplicative interaction and various types of correspondence analysis.problem with several restrictions related to the topology of nodes and arcs. The Understanding Biplots:diversity of implied constraints in real-life applications is a practical limit to the  Introduces theory and techniques which can be applied to problems from aresolution of such problems by approaches considering the partitioning problem variety of areas, including ecology, biostatistics, finance, demography and otherindependently from each additional restriction. This book focuses on constraint social sciences.satisfaction problems related to tree partitioning problems enriched by several  Provides novel techniques for the visualization of multidimensional data andadditional constraints that restrict the possible partitions topology. On the one hand, includes data mining techniques.this title focuses on the structural properties of tree partitioning constraints.  Uses applications from many fields including finance, biostatistics, ecology, demography. Understanding Computational Bayesian Using IBM® SPSS® Statistics for Research Statistics Methods and Social Science Statistics William M. Bolstad Warner Wiley SAGE 9780470046098  9781452217703  11.01.2010 02.04.2012 Oprawa: twarda Oprawa: miękka £ 79,50 £ 18,99A hands-on introduction to computational statistics from a Bayesian point of view Ideal either as a companion to a traditional statistics or research methods text or asProviding a solid grounding in statistics while uniquely covering the topics from a a stand-alone guide, Using SPSS for Research Methods and Social Statistics is aBayesian perspective, Understanding Computational Bayesian Statistics useful reference for those learning to use the SPSS software for the first time orsuccessfully guides readers through this new, cutting-edge approach. With its those with only basic knowledge about SPSS. This student-friendly resource showshands-on treatment of the topic, the book shows how samples can be drawn from readers how to use images and directions drawn from SPSS Version 20 and nowthe posterior distribution when the formula giving its shape is all that is known, and uses the latest version of the General Social Survey (GSS, 2010) as a secondaryhow Bayesian inferences can be based on these samples from the posterior. These data set.ideas are illustrated on common statistical models, including the multiple linearregression model, the hierarchical mean model, the logistic regression model, andthe proportional hazards model. The book begins with an outline of the similaritiesand differences between Bayesian and the likelihood approaches to statistics.Subsequent chapters present key techniques for using computer software to drawMonte Carlo samples from the incompletely known posterior distribution andperforming the Bayesian inference calculated from these samples. Visual Data Mining: The VisMiner Approach Russell K. Anderson Wiley 9781119967545  16.11.2012 Oprawa: twarda £ 45,00This book introduces a visual methodology for data mining demonstrating theapplication of methodology along with a sequence of exercises using VisMiner.VisMiner has been developed by the author and provides a powerful visual datamining tool enabling readers to visually evaluate models created from the data. Thisbook is designed as a hands-on work book to introduce the methodologies tostudents in data mining, advanced statistics, and business intelligence courses. Itprovides a set of tutorials, exercises, and case studies that support readers inlearning data mining 11