This document summarizes a lecture on Poisson regression for count data. It begins with an introduction to the Poisson distribution and its properties. It then discusses the framework of Poisson regression, using a log link function to model the Poisson parameter as a function of covariates. An example using elephant mating data is analyzed using Poisson regression. Model fitting, interpretation of coefficients, and obtaining fitted values are demonstrated. Finally, issues like overdispersion and zero-inflated models are discussed.
This document provides an introduction and overview of logistic regression. It describes why logistic regression is used when the dependent variable is limited and not continuous. Maximum likelihood estimation is used to estimate the coefficients. The coefficients can be interpreted as odds ratios. The performance of the logistic regression model is evaluated using measures like the model chi-square, percent correct predictions, and pseudo R-squared. Potential problems like omitted variables, irrelevant variables, and structural breaks are also discussed.
Stuck with your Regression Assignment? Get 24/7 help from tutors with Phd in the subject. Email us at support@helpwithassignment.com
Reach us at http://www.HelpWithAssignment.com
Big Data analysis involves building predictive models from high-dimensional data using techniques like variable selection, cross-validation, and regularization to avoid overfitting. The document discusses an example analyzing web browsing data to predict online spending, highlighting challenges with large numbers of variables. It also covers summarizing high-dimensional data through dimension reduction and model building for prediction versus causal inference.
This document provides an overview of maximum likelihood estimation (MLE). It discusses why MLE is needed for nonlinear models, the general steps for obtaining MLEs, and some key properties. The document also includes an example of calculating the MLE for a Poisson distribution in R. Key points covered include deriving the likelihood function, taking derivatives to find the MLE, and measuring uncertainty around the MLE estimate.
The document discusses simple linear regression and correlation methods. It defines deterministic and probabilistic models for describing the relationship between two variables. A simple linear regression model assumes a population regression line with intercept a and slope b, where observations may deviate from the line by some random error e. Key assumptions of the model are that e has a normal distribution with mean 0 and constant variance across values of x, and errors are independent. The slope b estimates the average change in y per unit change in x.
This document provides an introduction to Bayesian analysis and probabilistic modeling. It begins with an overview of Bayes' theorem and common probability distributions used in Bayesian modeling like the Bernoulli, binomial, beta, Dirichlet, and multinomial distributions. It then discusses how these distributions can be used in Bayesian modeling for problems like estimating probabilities based on observed data. Specifically, it explains how conjugate prior distributions allow the posterior distribution to be of the same family as the prior. The document concludes by discussing how neural networks can quantify classification uncertainty by outputting evidence for different classes modeled with a Dirichlet distribution.
This document presents a comparison of dimension reduction techniques for survival analysis, including principal component analysis (PCA), partial least squares (PLS), and random matrix approaches. Simulation data with 100 observations and 1000 covariates was generated to test the ability of each method to minimize bias and mean squared error in estimating survival functions. PCA and PLS were able to capture 50% of the variance by reducing the dimensions to 37. The estimated survival functions were compared to the true function over 5000 iterations. PLS had the lowest bias and mean squared error, followed by PCA, with the random matrix approaches performing worse.
This document provides an introduction and overview of logistic regression. It describes why logistic regression is used when the dependent variable is limited and not continuous. Maximum likelihood estimation is used to estimate the coefficients. The coefficients can be interpreted as odds ratios. The performance of the logistic regression model is evaluated using measures like the model chi-square, percent correct predictions, and pseudo R-squared. Potential problems like omitted variables, irrelevant variables, and structural breaks are also discussed.
Stuck with your Regression Assignment? Get 24/7 help from tutors with Phd in the subject. Email us at support@helpwithassignment.com
Reach us at http://www.HelpWithAssignment.com
Big Data analysis involves building predictive models from high-dimensional data using techniques like variable selection, cross-validation, and regularization to avoid overfitting. The document discusses an example analyzing web browsing data to predict online spending, highlighting challenges with large numbers of variables. It also covers summarizing high-dimensional data through dimension reduction and model building for prediction versus causal inference.
This document provides an overview of maximum likelihood estimation (MLE). It discusses why MLE is needed for nonlinear models, the general steps for obtaining MLEs, and some key properties. The document also includes an example of calculating the MLE for a Poisson distribution in R. Key points covered include deriving the likelihood function, taking derivatives to find the MLE, and measuring uncertainty around the MLE estimate.
The document discusses simple linear regression and correlation methods. It defines deterministic and probabilistic models for describing the relationship between two variables. A simple linear regression model assumes a population regression line with intercept a and slope b, where observations may deviate from the line by some random error e. Key assumptions of the model are that e has a normal distribution with mean 0 and constant variance across values of x, and errors are independent. The slope b estimates the average change in y per unit change in x.
This document provides an introduction to Bayesian analysis and probabilistic modeling. It begins with an overview of Bayes' theorem and common probability distributions used in Bayesian modeling like the Bernoulli, binomial, beta, Dirichlet, and multinomial distributions. It then discusses how these distributions can be used in Bayesian modeling for problems like estimating probabilities based on observed data. Specifically, it explains how conjugate prior distributions allow the posterior distribution to be of the same family as the prior. The document concludes by discussing how neural networks can quantify classification uncertainty by outputting evidence for different classes modeled with a Dirichlet distribution.
This document presents a comparison of dimension reduction techniques for survival analysis, including principal component analysis (PCA), partial least squares (PLS), and random matrix approaches. Simulation data with 100 observations and 1000 covariates was generated to test the ability of each method to minimize bias and mean squared error in estimating survival functions. PCA and PLS were able to capture 50% of the variance by reducing the dimensions to 37. The estimated survival functions were compared to the true function over 5000 iterations. PLS had the lowest bias and mean squared error, followed by PCA, with the random matrix approaches performing worse.
This document discusses statistical tests for comparing groups on continuous and categorical outcomes. For binary outcomes, it describes chi-square tests, logistic regression, McNemar's tests, and conditional logistic regression for independent and correlated groups. For continuous outcomes, it discusses t-tests, ANOVA, linear regression, paired t-tests, repeated measures ANOVA, mixed models, and non-parametric alternatives. It also provides examples of calculating odds ratios, standard errors, and performing hypothesis tests like the two-sample t-test.
This document provides an overview of logistic regression. It begins with examples of why linear regression is not appropriate for binary outcome data and introduces the logistic function. The document explains that logistic regression models the log-odds of an event using a linear regression approach. Key aspects covered include the Bernoulli distribution, maximum likelihood estimation, odds ratios, and interpreting logistic regression coefficients. Examples using real data on news story selection are provided to illustrate fitting and interpreting a logistic regression model.
This document proposes a method for linear regression on symbolic data where each observation is represented by a Gaussian distribution. It derives the likelihood function for such "Gaussian symbols" and shows that it can be maximized using gradient descent. Simulation results demonstrate that the maximum likelihood estimator performs better than a naive least squares regression on the mean of each symbol. The method extends classical linear regression to the symbolic data setting.
This document provides an overview and schedule for an advanced econometrics training using Stata. The training covers topics such as hypothesis testing, multiple regression, time series models, panel data models, and difference-in-differences. It discusses assumptions of classical linear regression models and how to perform statistical inference using estimates of variance, standard error, and hypothesis testing. The document explains how to construct t-statistics and compare them to critical values from the t-distribution to test hypotheses about population parameters.
Foundations of Statistics for Ecology and Evolution. 4. Maximum LikelihoodAndres Lopez-Sepulcre
1. Maximum Likelihood Estimation
- The abductive method
- How to fit a model to data
2. Calculating parameter Uncertainty
3. Comparing Multiple Hypotheses
- Alternatives beyond rejection
- Parsimony and information
This document provides an introduction to bootstrap methods and Markov chains. It discusses how bootstrap can be used to estimate properties of a statistic like mean or variance when the sample is small and assumptions of the central limit theorem may not apply. The basic bootstrap approach resamples the original sample with replacement to create new bootstrap samples and estimates the statistic for each. Markov chains are defined as stochastic processes where the next state only depends on the current state. An example of a 2-state Markov chain is provided along with notation for transition probabilities and computing unconditional probabilities. The document also discusses stationary distributions for Markov chains.
The document discusses maximum likelihood estimation. It begins by explaining that maximum likelihood chooses parameter values that make the observed data most probable given a statistical model. This provides a justification for estimation techniques like least squares regression. The document provides an example of estimating a population proportion from a sample. It then generalizes maximum likelihood to cover a wide range of models and estimation problems. It discusses properties like consistency, efficiency, and how to conduct hypothesis tests based on maximum likelihood. Numerical optimization techniques are often required to find maximum likelihood estimates for complex models.
This document discusses logistic regression for categorical response variables. It provides examples of binary and ordinal categorical response variables like whether someone smokes (yes/no) or the success of a medical treatment (survives/dies). It then demonstrates how to perform binary logistic regression in R to predict a binary outcome like gender from height. Key aspects covered include interpreting the logistic regression coefficients, plotting the logistic curve, and calculating odds ratios to compare two groups.
The document discusses various methods for modeling input distributions in simulation models, including trace-driven simulation, empirical distributions, and fitting theoretical distributions to real data. It provides examples of several continuous and discrete probability distributions commonly used in simulation, including the exponential, normal, gamma, Weibull, binomial, and Poisson distributions. Key parameters and properties of each distribution are defined. Methods for selecting an appropriate input distribution based on summary statistics of real data are also presented.
Nonlinear regression functions allow the regression model to be nonlinear in one or more independent variables. There are two main approaches to modeling nonlinear relationships: polynomials and logarithmic transformations. Polynomials approximate the relationship with higher-order terms of the independent variable, such as quadratic or cubic terms. Logarithmic transformations model relationships in percentage terms by taking logarithms of variables. Both approaches can be estimated using ordinary least squares regression.
This document summarizes research on the First-Order Integer-Valued Autoregressive (INAR(1)) process. It describes the INAR(1) model, including how it represents lag-one dependence between integer-valued random variables. It also discusses four estimation methods for the INAR(1) parameters (α and λ): Yule-Walker, Conditional Least Squares, Maximum Likelihood, and Whittle estimation. Simulation results show that Conditional Maximum Likelihood generally has the lowest bias, making it the best estimation method among the four.
ISM_Session_5 _ 23rd and 24th December.pptxssuser1eba67
The document discusses random variables and their probability distributions. It defines discrete and continuous random variables and their key characteristics. Discrete random variables can take on countable values while continuous can take any value in an interval. Probability distributions describe the probabilities of a random variable taking on different values. The mean and variance are discussed as measures of central tendency and variability. Joint probability distributions are introduced for two random variables. Examples and homework problems are also provided.
Calibrating Probability with Undersampling for Unbalanced ClassificationAndrea Dal Pozzolo
This study examines how undersampling affects posterior probability estimates in unbalanced classification tasks. It shows that undersampling warps the posterior probabilities away from the true probabilities. However, the study presents a method to correct the warped probabilities using a simple formula, which provides calibrated probabilities without loss of predictive performance. Experiments on real-world datasets demonstrate that the corrected probabilities have better calibration than uncorrected probabilities while maintaining ranking quality.
Eigenvalues for HIV-1 dynamic model with two delaysIOSR Journals
This document presents a new approach to solve the characteristic equation of an HIV-1 infection dynamical system with two delays. The authors develop a series expansion to approximate the eigenvalues (roots) of the nonlinear characteristic equation. They derive the characteristic equation for the linearized HIV-1 model and nondimensionalize the equation. This allows them to express the eigenvalues as a perturbation of the logarithm of a parameter and derive an equation for the perturbation term. The goal is to make the truncated series more computationally efficient for evaluating the eigenvalues.
This document summarizes a journal article that proposes an alternative approach to variable selection called the KL adaptive lasso. The KL adaptive lasso replaces the squared error loss used in traditional adaptive lasso with Kullback-Leibler divergence loss. The paper shows that the KL adaptive lasso enjoys oracle properties, meaning it performs as well as if the true underlying model was given. Specifically, it consistently selects the true variables and estimates their coefficients at optimal rates. The KL adaptive lasso can also be solved using efficient algorithms like LARS. The approach is extended to generalized linear models, and theoretical properties are discussed.
This document discusses various modeling approaches for non-life insurance tariffication including frequency-severity models, Tweedie regression models, and high-dimensional modeling techniques like ridge regression and the LASSO. It compares individual risk and collective risk models, explores the impact of the Tweedie parameter, and applies regularization methods to insurance data.
Bayesian inference for mixed-effects models driven by SDEs and other stochast...Umberto Picchini
An important, and well studied, class of stochastic models is given by stochastic differential equations (SDEs). In this talk, we consider Bayesian inference based on measurements from several individuals, to provide inference at the "population level" using mixed-effects modelling. We consider the case where dynamics are expressed via SDEs or other stochastic (Markovian) models. Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that account for (i) the intrinsic random variability in the latent states dynamics, as well as (ii) the variability between individuals, and also (iii) account for measurement error. This flexibility gives rise to methodological and computational difficulties.
Fully Bayesian inference for nonlinear SDEMEMs is complicated by the typical intractability of the observed data likelihood which motivates the use of sampling-based approaches such as Markov chain Monte Carlo. A Gibbs sampler is proposed to target the marginal posterior of all parameters of interest. The algorithm is made computationally efficient through careful use of blocking strategies, particle filters (sequential Monte Carlo) and correlated pseudo-marginal approaches. The resulting methodology is is flexible, general and is able to deal with a large class of nonlinear SDEMEMs [1]. In a more recent work [2], we also explored ways to make inference even more scalable to an increasing number of individuals, while also dealing with state-space models driven by other stochastic dynamic models than SDEs, eg Markov jump processes and nonlinear solvers typically used in systems biology.
[1] S. Wiqvist, A. Golightly, AT McLean, U. Picchini (2020). Efficient inference for stochastic differential mixed-effects models using correlated particle pseudo-marginal algorithms, CSDA, https://doi.org/10.1016/j.csda.2020.107151
[2] S. Persson, N. Welkenhuysen, S. Shashkova, S. Wiqvist, P. Reith, G. W. Schmidt, U. Picchini, M. Cvijovic (2021). PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models, bioRxiv doi:10.1101/2021.07.01.450748.
This document discusses regression with frailty in survival analysis using the Cox proportional hazards model. It introduces survival analysis concepts like the hazard function and survival function. It then describes how to incorporate frailty, a random effect, into the Cox model to account for clustering in survival times. The Newton-Raphson method is used to estimate model parameters by maximizing the penalized partial likelihood. A simulation study applies this approach to data on infections in kidney patients.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
This document discusses statistical tests for comparing groups on continuous and categorical outcomes. For binary outcomes, it describes chi-square tests, logistic regression, McNemar's tests, and conditional logistic regression for independent and correlated groups. For continuous outcomes, it discusses t-tests, ANOVA, linear regression, paired t-tests, repeated measures ANOVA, mixed models, and non-parametric alternatives. It also provides examples of calculating odds ratios, standard errors, and performing hypothesis tests like the two-sample t-test.
This document provides an overview of logistic regression. It begins with examples of why linear regression is not appropriate for binary outcome data and introduces the logistic function. The document explains that logistic regression models the log-odds of an event using a linear regression approach. Key aspects covered include the Bernoulli distribution, maximum likelihood estimation, odds ratios, and interpreting logistic regression coefficients. Examples using real data on news story selection are provided to illustrate fitting and interpreting a logistic regression model.
This document proposes a method for linear regression on symbolic data where each observation is represented by a Gaussian distribution. It derives the likelihood function for such "Gaussian symbols" and shows that it can be maximized using gradient descent. Simulation results demonstrate that the maximum likelihood estimator performs better than a naive least squares regression on the mean of each symbol. The method extends classical linear regression to the symbolic data setting.
This document provides an overview and schedule for an advanced econometrics training using Stata. The training covers topics such as hypothesis testing, multiple regression, time series models, panel data models, and difference-in-differences. It discusses assumptions of classical linear regression models and how to perform statistical inference using estimates of variance, standard error, and hypothesis testing. The document explains how to construct t-statistics and compare them to critical values from the t-distribution to test hypotheses about population parameters.
Foundations of Statistics for Ecology and Evolution. 4. Maximum LikelihoodAndres Lopez-Sepulcre
1. Maximum Likelihood Estimation
- The abductive method
- How to fit a model to data
2. Calculating parameter Uncertainty
3. Comparing Multiple Hypotheses
- Alternatives beyond rejection
- Parsimony and information
This document provides an introduction to bootstrap methods and Markov chains. It discusses how bootstrap can be used to estimate properties of a statistic like mean or variance when the sample is small and assumptions of the central limit theorem may not apply. The basic bootstrap approach resamples the original sample with replacement to create new bootstrap samples and estimates the statistic for each. Markov chains are defined as stochastic processes where the next state only depends on the current state. An example of a 2-state Markov chain is provided along with notation for transition probabilities and computing unconditional probabilities. The document also discusses stationary distributions for Markov chains.
The document discusses maximum likelihood estimation. It begins by explaining that maximum likelihood chooses parameter values that make the observed data most probable given a statistical model. This provides a justification for estimation techniques like least squares regression. The document provides an example of estimating a population proportion from a sample. It then generalizes maximum likelihood to cover a wide range of models and estimation problems. It discusses properties like consistency, efficiency, and how to conduct hypothesis tests based on maximum likelihood. Numerical optimization techniques are often required to find maximum likelihood estimates for complex models.
This document discusses logistic regression for categorical response variables. It provides examples of binary and ordinal categorical response variables like whether someone smokes (yes/no) or the success of a medical treatment (survives/dies). It then demonstrates how to perform binary logistic regression in R to predict a binary outcome like gender from height. Key aspects covered include interpreting the logistic regression coefficients, plotting the logistic curve, and calculating odds ratios to compare two groups.
The document discusses various methods for modeling input distributions in simulation models, including trace-driven simulation, empirical distributions, and fitting theoretical distributions to real data. It provides examples of several continuous and discrete probability distributions commonly used in simulation, including the exponential, normal, gamma, Weibull, binomial, and Poisson distributions. Key parameters and properties of each distribution are defined. Methods for selecting an appropriate input distribution based on summary statistics of real data are also presented.
Nonlinear regression functions allow the regression model to be nonlinear in one or more independent variables. There are two main approaches to modeling nonlinear relationships: polynomials and logarithmic transformations. Polynomials approximate the relationship with higher-order terms of the independent variable, such as quadratic or cubic terms. Logarithmic transformations model relationships in percentage terms by taking logarithms of variables. Both approaches can be estimated using ordinary least squares regression.
This document summarizes research on the First-Order Integer-Valued Autoregressive (INAR(1)) process. It describes the INAR(1) model, including how it represents lag-one dependence between integer-valued random variables. It also discusses four estimation methods for the INAR(1) parameters (α and λ): Yule-Walker, Conditional Least Squares, Maximum Likelihood, and Whittle estimation. Simulation results show that Conditional Maximum Likelihood generally has the lowest bias, making it the best estimation method among the four.
ISM_Session_5 _ 23rd and 24th December.pptxssuser1eba67
The document discusses random variables and their probability distributions. It defines discrete and continuous random variables and their key characteristics. Discrete random variables can take on countable values while continuous can take any value in an interval. Probability distributions describe the probabilities of a random variable taking on different values. The mean and variance are discussed as measures of central tendency and variability. Joint probability distributions are introduced for two random variables. Examples and homework problems are also provided.
Calibrating Probability with Undersampling for Unbalanced ClassificationAndrea Dal Pozzolo
This study examines how undersampling affects posterior probability estimates in unbalanced classification tasks. It shows that undersampling warps the posterior probabilities away from the true probabilities. However, the study presents a method to correct the warped probabilities using a simple formula, which provides calibrated probabilities without loss of predictive performance. Experiments on real-world datasets demonstrate that the corrected probabilities have better calibration than uncorrected probabilities while maintaining ranking quality.
Eigenvalues for HIV-1 dynamic model with two delaysIOSR Journals
This document presents a new approach to solve the characteristic equation of an HIV-1 infection dynamical system with two delays. The authors develop a series expansion to approximate the eigenvalues (roots) of the nonlinear characteristic equation. They derive the characteristic equation for the linearized HIV-1 model and nondimensionalize the equation. This allows them to express the eigenvalues as a perturbation of the logarithm of a parameter and derive an equation for the perturbation term. The goal is to make the truncated series more computationally efficient for evaluating the eigenvalues.
This document summarizes a journal article that proposes an alternative approach to variable selection called the KL adaptive lasso. The KL adaptive lasso replaces the squared error loss used in traditional adaptive lasso with Kullback-Leibler divergence loss. The paper shows that the KL adaptive lasso enjoys oracle properties, meaning it performs as well as if the true underlying model was given. Specifically, it consistently selects the true variables and estimates their coefficients at optimal rates. The KL adaptive lasso can also be solved using efficient algorithms like LARS. The approach is extended to generalized linear models, and theoretical properties are discussed.
This document discusses various modeling approaches for non-life insurance tariffication including frequency-severity models, Tweedie regression models, and high-dimensional modeling techniques like ridge regression and the LASSO. It compares individual risk and collective risk models, explores the impact of the Tweedie parameter, and applies regularization methods to insurance data.
Bayesian inference for mixed-effects models driven by SDEs and other stochast...Umberto Picchini
An important, and well studied, class of stochastic models is given by stochastic differential equations (SDEs). In this talk, we consider Bayesian inference based on measurements from several individuals, to provide inference at the "population level" using mixed-effects modelling. We consider the case where dynamics are expressed via SDEs or other stochastic (Markovian) models. Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that account for (i) the intrinsic random variability in the latent states dynamics, as well as (ii) the variability between individuals, and also (iii) account for measurement error. This flexibility gives rise to methodological and computational difficulties.
Fully Bayesian inference for nonlinear SDEMEMs is complicated by the typical intractability of the observed data likelihood which motivates the use of sampling-based approaches such as Markov chain Monte Carlo. A Gibbs sampler is proposed to target the marginal posterior of all parameters of interest. The algorithm is made computationally efficient through careful use of blocking strategies, particle filters (sequential Monte Carlo) and correlated pseudo-marginal approaches. The resulting methodology is is flexible, general and is able to deal with a large class of nonlinear SDEMEMs [1]. In a more recent work [2], we also explored ways to make inference even more scalable to an increasing number of individuals, while also dealing with state-space models driven by other stochastic dynamic models than SDEs, eg Markov jump processes and nonlinear solvers typically used in systems biology.
[1] S. Wiqvist, A. Golightly, AT McLean, U. Picchini (2020). Efficient inference for stochastic differential mixed-effects models using correlated particle pseudo-marginal algorithms, CSDA, https://doi.org/10.1016/j.csda.2020.107151
[2] S. Persson, N. Welkenhuysen, S. Shashkova, S. Wiqvist, P. Reith, G. W. Schmidt, U. Picchini, M. Cvijovic (2021). PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models, bioRxiv doi:10.1101/2021.07.01.450748.
This document discusses regression with frailty in survival analysis using the Cox proportional hazards model. It introduces survival analysis concepts like the hazard function and survival function. It then describes how to incorporate frailty, a random effect, into the Cox model to account for clustering in survival times. The Newton-Raphson method is used to estimate model parameters by maximizing the penalized partial likelihood. A simulation study applies this approach to data on infections in kidney patients.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
9_Poisson_printable.pdf
1. Week 9:
Count Data - Poisson Regression
Applied Statistical Analysis II
Jeffrey Ziegler, PhD
Assistant Professor in Political Science & Data Science
Trinity College Dublin
Spring 2023
3. Introduction to Poisson distribution
Let X be distributed as a Poisson random variable with single
parameter λ
P(X = k) =
e−kλk
k!
k ∈ (0, 1, 2, 3, 4, · · · )
X is a discrete random
variable with
probabilities expressed
in whole #s
2 29
4. Introduction to Poisson distribution
If Y ∼ Poisson(λ), then
E(Y) = λ and Var(Y) = λ
Mean and variance are equal, and variance is tied to mean
If mean of Y increases with covariate X, so does variance of Y
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5. Framework: Poisson regression
Poisson regression model:
ln(λi) = β0 + β1X1i + β2X2i + · · · + βkXki
where
λi = eβ0+β1X1i+β2X2i+···+βkXki
Poisson parameter λi depends on covariates of each
observation
I So, each observation can have its own mean
Again, mean depends on covariates, and variance depends
on covariates
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6. Background: Poisson regression
Poisson regression is another generalized linear model
Instead of a log function of Bernoulli parameter πi (logistic
regression), we use a log function of Poisson parameter λi
λi > 0 → −∞ < ln(λi) < ∞
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7. Background: Poisson regression
The logit function in logistic model and log function in
Poisson model are called the link functions for these GLMs
In this modeling, we assume that ln(λi) is linearly related to
independent variables
I And that mean and variance are equal for a given λi
An iterative process is used to solve the likelihood equations
and get maximum likelihood estimates (MLE)
I If you’re interested in this specifically applied with Poisson,
check out Gill (2001)
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8. Zoology Example: mating of elephants
There is competition for female mates between young and
old male elephants1
Male elephants continue to grow throughout their lives →
older elephants are larger and Pr(Successful mating) ↑
Variables:
I Response: # of
mates
I Predictor: Age of
male elephant
(years)
1
Source: J. H. Poole, Mate Guarding, Reproductive Success and Female Choice in
African Elephants, Animal Behavior 37 (1989): 842-49
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9. Zoology Example: mating of elephants
Let’s look at jitter scatterplot first
30 35 40 45 50
0
2
4
6
8
Age
Number
of
Mates
It looks like the number
of mates tends to be
higher for older
elephants
Seems to be more
variability in the
number of mates as
age increases
Elephants of age 30
have between 0 and 4
mates
Elephants of age 45
have between 0 and 9
mates
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10. Zoology Example: Poisson regression model
If dispersion (variance) ↑ with mean for a count response,
then Poisson regression may be a good modeling choice
I Why? Because variance is tied to mean!
ln(λi) = β̂0 + β̂1X
1 elephant_poisson <− glm ( Matings ~ Age , data=elephant , family =poisson )
(Intercept) −1.582∗∗
(0.545)
Age_in_Years 0.069∗∗∗
(0.014)
AIC 156.458
BIC 159.885
Log Likelihood -76.229
Deviance 51.012
Num. obs. 41
∗∗∗p < 0.001, ∗∗p < 0.01, ∗p < 0.05
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11. Example: Poisson regression curve
Add fitted curve to scatterplot:
1 coeffs <− coefficients (
elephant_poisson )
2 xvalues <− sort ( elephant$
Age )
3 means <− exp ( coeffs [ 1 ] +
coeffs [ 2 ] * xvalues )
4 lines ( xvalues , means , l t y
=2 , col = " red " )
30 35 40 45 50
0
2
4
6
8
Age
Number
of
Mates
Poisson regression is a nonlinear model for E[Y]
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12. Example: significance test
(Intercept) −1.582∗∗
(0.545)
Age_in_Years 0.069∗∗∗
(0.014)
AIC 156.458
BIC 159.885
Log Likelihood -76.229
Deviance 51.012
Num. obs. 41
∗∗∗p < 0.001, ∗∗p < 0.01, ∗p < 0.05
Age is a reliable and
positive predictor of # of
mates for an elephant
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13. Example: parameter interpretation
One covariate: ln(λi) = β0 + β1Xi
β0 : eβ0 is mean of Poisson distribution when X = 0
β1 : Increasing X by 1 unit has a multiplicative effect on the
mean of Poisson by eβ1
λ(x+1)
λ(x)
=
eβ0+β1(x+1)
eβ0+β1x
=
eβ
0eβ1xebeta1
eβ0 eβ1x
= eβ1
λ(x+1) = λ(x)eβ1
If β1 > 0, then expected count increases as X increases
If β1 < 0, then expected count decreases as X increases
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14. Example: parameter interpretation
For the elephant data:
β̂0 : No inherent meaning in the context of the data since
age= 0 is not meaningful, outside of range of possible data
Since coefficient is positive, expected # of mates ↑ with age
β̂1 : An increase of 1 year in age increases expected number
of elephant mates by a multiplicative factor of e0.06859 ≈ 1.07
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15. Example: Getting fitted values
Fitted model:
λi = eβ̂0+β̂1Xi
What is fitted count for an elephant of 30 years?
Estimated mean number of mates = 1.6
Estimated variance in number of mates = 1.6
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16. Example: Estimating fitted values
λi = eβ̂0+β̂1Xi
What is fitted count for an elephant of 45 years?
Estimated mean number of mates = 4.5
Estimated variance in number of mates = 4.5
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17. Getting fitted values in R
1 predicted_values <− cbind ( predict ( elephant_poisson , data . frame ( Age = seq (25 , 55 , 5) ) ,
type=" response " , se . f i t =TRUE ) , data . frame ( Age = seq (25 , 55 , 5) ) )
2 # create lower and upper bounds for CIs
3 predicted_values$lowerBound <− predicted_values$ f i t − 1.96 * predicted_values$se . f i t
4 predicted_values$upperBound <− predicted_values$ f i t + 1.96 * predicted_values$se . f i t
5
10
3
0
4
0
5
0
Age (Years)
Predicted
#
of
mates
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18. Assumptions: Over-dispersion
Assuming that model is correctly specified, assumption that
conditional variance is equal to conditional mean should be
checked
There are several tests including the likelihood ratio test of
over-dispersion parameter alpha by running same model
using negative binomial distribution
R package AER provides many functions for count data
including dispersiontest for testing over-dispersion
One common cause of over-dispersion is excess zeros, which
in turn are generated by an additional data generating
process
In this situation, zero-inflated model should be considered
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19. Zero inflatied poisson: # of mates
# of mates
Frequency
0 2 4 6 8
0
2
4
6
8
10
12
14
Though predictors do
seem to impact
distribution of
elephant mates,
Poisson regression
may not be a good fit
(large # of 0s)
We’ll check by
I Running an
over-dispersion
test
I Fit a zero-inflated
Poisson
regression
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20. Over-dispersion test in R
1 # check equal variance assumption
2 dispersiontest ( elephant_poisson )
Overdispersion test
data: elephant_poisson
z = 0.49631, p-value = 0.3098
alternative hypothesis: true dispersion is greater than 1
sample estimates:
dispersion
1.107951
Doesn’t seem like we really need a ZIP model, but we’ll do it
anyway...
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21. Intuition behind Zero-inflated Poisson
In terms of fitting the model, we combine logistic regression
model and Poisson regression model
ZIP model:
I We model probability of being a perfect zero as a logistic
regression
I Then, we model Poisson part as a Poisson regression
There are two generalized linear models working together to
explain data
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22. ZIP model in R
R contributed package “pscl" contains the function zeroinfl:
1 # same equation for l o g i t and poisson
2 z e r o i n f l _poisson <− z e r o i n f l ( Matings ~ Age , data=elephant , dist =" poisson " )
Count model: (Intercept) −1.45∗∗
(0.55)
Count model: Age_in_Years 0.07∗∗∗
(0.01)
Zero model: (Intercept) 222.47
(232.27)
Zero model: Age_in_Years −8.12
(8.44)
AIC 157.88
Log Likelihood -74.94
Num. obs. 41
Further evidence we don’t really need zero-inflated model
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23. Exposure Variables: Offset parameter
Count data often have an exposure variable, which indicates
# of times event could have happened
This variable should be incorporated into a Poisson model
using offset option
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24. Ex: Food insecurity in Tanzania and Mozambique
Survey data from households about agriculture
Covered such things as:
I Household features (e.g. construction materials used,
number of household members)
I Agricultural practices (e.g. water usage)
I Assets (e.g. number and types of livestock)
I Details about the household members
Collected through interviews conducted between Nov. 2016 -
June 2017 using forms downloaded to Android Smartphones
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25. What predicts owning more livestock?
Outcome: Livestock count [1-5]
Predictors:
I # of years lived in village
I # of people who live in household
I Whether they’re apart of a farmer cooperative
I Conflict with other farmers
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26. Owning Livestock: Estimate poisson regression
1 # load data
2 s a f i <− read . csv ( " https : //raw .
githubusercontent . com/ASDS−
TCD/ S t a t s I I _Spring2023/main
/datasets/SAFI . csv " ,
stringsAsFactors = T )
1
2 # estimate poisson regression
model
3 s a f i _poisson <− glm ( l i v _count ~
no_membrs + years_ l i v +
memb_assoc + affect _
conflicts , data= safi ,
family =poisson )
(Intercept) 0.40∗∗
(0.15)
no_membrs 0.03
(0.02)
years_liv 0.01∗
(0.00)
memb_assoc_yes −0.03
(0.16)
affect_conflicts_frequently 0.09
(0.24)
affect_conflicts_more_once 0.14
(0.15)
affect_conflicts_once 0.09
(0.25)
AIC 417.98
BIC 438.11
Log Likelihood −201.99
Deviance 54.52
N 131
∗∗∗p < 0.001; ∗∗p < 0.01; ∗p < 0.05
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27. Owning Livestock: Poisson regression curve
Add fitted curve to scatterplot:
0 20 40 60 80
1
2
3
4
5
Years lived in village
Number
of
livestock
As # of years in village ↑, ↑ expected # of livestock
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28. Owning Livestock: Fitted values in R
1 s a f i _ex <− data . frame (no_membrs = rep (mean( s a f i $no_membrs) , 6) ,
2 years_ l i v = seq ( 1 , 60 , 10) ,
3 memb_assoc = rep ( "no" , 6) ,
4 affect _ c o n f l i c t s = rep ( " never " , 6) )
5 pred_ s a f i <− cbind ( predict ( s a f i _poisson , s a f i _ex , type= " response " , se . f i t =TRUE ) , s a f i _ex )
1.5
2.0
2.5
3.0 0
1
0
2
0
3
0
4
0
5
0
Years in village
Predicted
#
of
livestock
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29. Owning Livestock: Over-dispersion
1 dispersiontest ( s a f i _poisson )
Overdispersion test
data: safi_poisson
z = -12.433, p-value = 1
alternative hypothesis: true dispersion is greater than 1
sample estimates:
dispersion
0.4130252
Don’t really need a ZIP model
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30. Wrap Up
In this lesson, we went over how to...
Estimate and interpret a Poisson regression for count data
Next time, we’ll talk about...
Duration models
Censoring & truncation
Selection
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