This is a student-created document on multiple imputation focusing on the two major approaches of modeling missing data: the joint and conditional approaches.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
Imputation techniques for missing data in clinical trialsNitin George
Missing data are unavoidable in clinical and epidemiological researches. Missing data leads to bias and loss of information in research analysis. Usually we are not aware of missing data techniques because we are depending on some software’s. The objective of this seminar is to introduce different missing data mechanisms and imputation techniques for missing data with the help of examples.
Classification of mathematical modeling,
Classification based on Variation of Independent Variables,
Static Model,
Dynamic Model,
Rigid or Deterministic Models,
Stochastic or Probabilistic Models,
Comparison Between Rigid and Stochastic Models
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Kazuki Yoshida
Presentation at the Epidemiology Congress of Americas 2016.
https://epiresearch.org/2016-meeting/submitted-abstract-sessions/pharmacoepidemiology-estimation-of-treatment/
Paper: http://journals.lww.com/epidem/Abstract/publishahead/Matching_weights_to_simultaneously_compare_three.98901.aspx (email me at kazukiyoshida@mail.harvard.edu)
Simulation code: https://github.com/kaz-yos/mw
Tutorial: http://rpubs.com/kaz_yos/matching-weights
Imputation techniques for missing data in clinical trialsNitin George
Missing data are unavoidable in clinical and epidemiological researches. Missing data leads to bias and loss of information in research analysis. Usually we are not aware of missing data techniques because we are depending on some software’s. The objective of this seminar is to introduce different missing data mechanisms and imputation techniques for missing data with the help of examples.
Classification of mathematical modeling,
Classification based on Variation of Independent Variables,
Static Model,
Dynamic Model,
Rigid or Deterministic Models,
Stochastic or Probabilistic Models,
Comparison Between Rigid and Stochastic Models
Matching Weights to Simultaneously Compare Three Treatment Groups: a Simulati...Kazuki Yoshida
Presentation at the Epidemiology Congress of Americas 2016.
https://epiresearch.org/2016-meeting/submitted-abstract-sessions/pharmacoepidemiology-estimation-of-treatment/
Paper: http://journals.lww.com/epidem/Abstract/publishahead/Matching_weights_to_simultaneously_compare_three.98901.aspx (email me at kazukiyoshida@mail.harvard.edu)
Simulation code: https://github.com/kaz-yos/mw
Tutorial: http://rpubs.com/kaz_yos/matching-weights
SheffieldR July Meeting - Multiple Imputation with Chained Equations (MICE) p...Paul Richards
Presentation given by Rich Jacques, talking thorough the use of the package "mice", which eases the pain of doing multiple imputations for analysis with incomplete datasets
Doing Analytics Right - Building the Analytics EnvironmentTasktop
Implementing analytics for development processes is challenging. As in discussed in the previous webinars, the right analytics are determined by the goals of the organization, not by the available data. So implementing your analytics solutions will require an efficient analytics and data architecture, including the ability to combine and stage data from heterogeneous sources. An architecture that excludes the ability to gain access to the necessary data will create a barrier to deploying your newly designed analytics program, and will force you back into the “light is brighter here” anti-pattern.
This webinar will describe the technical considerations of implementing the data architecture for your analytics program, and explain how Tasktop can help.
Interoperability defined by its reason d'êtreAALForum
Presentation by Paul Valckenaers and Patrick De Maziére during the workshop Interoperability defined by its reason d'être by Paul Valckenaers - AAL Forum 2015
Open data is a crucial prerequisite for inventing and disseminating the innovative practices needed for agricultural development. To be usable, data must not just be open in principle—i.e., covered by licenses that allow re-use. Data must also be published in a technical form that allows it to be integrated into a wide range of applications. The webinar will be of interest to any institution seeking ways to publish and curate data in the Linked Data cloud.
This webinar describes the technical solutions adopted by a widely diverse global network of agricultural research institutes for publishing research results. The talk focuses on AGRIS, a central and widely-used resource linking agricultural datasets for easy consumption, and AgriDrupal, an adaptation of the popular, open-source content management system Drupal optimized for producing and consuming linked datasets.
Agricultural research institutes in developing countries share many of the constraints faced by libraries and other documentation centers, and not just in developing countries: institutions are expected to expose their information on the Web in a re-usable form with shoestring budgets and with technical staff working in local languages and continually lured by higher-paying work in the private sector. Technical solutions must be easy to adopt and freely available.
Reproducible and citable data and models: an introduction.FAIRDOM
Prepared and presented by Carole Goble (University of Manchester), Wolfgang Mueller (HITS), Dagmar Waltermath (University of Rostock), at the Reproducible and Citable Data and Models Workshop, Warnemünde, Germany. September 14th - 16th 2015.
We make sense of the world around us by turning data into information. For years, research in fields such as machine learning (ML), data mining, databases, information retrieval, natural language processing, and speech recognition have steadily improved their techniques for revealing the information lying within otherwise opaque datasets. But computer science is now on the verge of a new era in data analysis because of several recent developments, including: the rise of the warehouse-scale computer, the massive explosion in online data, the increasing diversity and time-sensitivity of queries, and the advent of crowdsourcing. Together these trends — often referred to collectively as Big Data — have the potential for ushering in a new era in data analysis, but to realize this opportunity requires us to confront several significant scientific challenges. This talk will discuss some of these challenges in the context of academic and industrial research in the United States.
Graph Analytics (or network analytics), is an area of analysis with numerous applications that increasingly draws more and more attention. From fraud detection and money laundering, illegal transactions and other forms of financial crime, to identify key influencers in social networks, communities of frequently interacting individuals and route optimisation or even bioinformatics; graph analytics offer a vast variety of solutions that keep on evolving on a daily basis; allowing for experts in various fields to tackle every day challenges, extract insights and drive decision making.
Mainly, Graph Analytics is divided into 4 categories:
* Path Analytics;
* Connectivity Analytics;
* Centrality Analytics, and;
* Community Detection analytics;
each of which relies on different algorithms and address different problems.
So, how does one apply these techniques effectively in order to drive hypothesis testing and, eventually, the extraction of actionable insights?
What are the steps that should be followed?
What is the impact of visualisation tools in this process?
How should we sample from graphs?
What tools should one use or be familiar with?
Furthermore, how can scalable and high-quality production-ready solutions be implemented that apply Graph Analytics, giving direct access to visualisations and on-demand analytics’ dashboards, which can serve as an intuitive and amenable means of information interpretation?
In this talk, we present and discuss the different categories of graph analytics and their areas of application. In addition, to address the above questions, we define a methodology for the application of these analytics technologies through example use-cases, studying the steps that need to be followed before assumptions can be confirmed and insights can be extracted.
Finally, we will discuss how distributed programming models, such as Spargel, have been developed to allow for the adoption of graph analytics algorithms by frameworks like Apache Spark and Apache Flink; the challenges and limitations that come with their adoption by these frameworks, and how one can build scalable distributed graph analytics solutions using them.
Paul Groth: Data Analysis in a Changing Discourse: The Challenges of Scholarl...COST Action TD1210
Paul Groth (Elsevier) “Data Analysis in a Changing Discourse: The Challenges of Scholarly Communication“
Presentation at the KnoweScape workshop "Evolution and variation of classification systems" March 4-5, 2015 Amsterdam
Webinar - Harness the Power of Data with Tableau - 2016-02-18TechSoup
Learn how to harness the power of data to tell your organization’s story with Tableau! Join Tech Impact's Jordan McCarthy and learn how to use Tableau to collect data in more meaningful ways and understand the science behind data analysis. We show you easy tips to maneuver through this data analytics tool to gain a better understanding of your nonprofit or library’s data.
Graphical explanation of causal mediation analysisKazuki Yoshida
The notion of the total effect decomposition into the natural direct effect and the natural indirect effect can be facilitated by intuitively understanding the nested counterfactual Y(1,M(0)).
What is the Expectation Maximization (EM) Algorithm?Kazuki Yoshida
Review of Do and Batzoglou. "What is the expectation maximization algorith?" Nat. Biotechnol. 2008;26:897. Also covers the Data Augmentation and Stan implementation. Resources at https://github.com/kaz-yos/em_da_repo
Propensity Score Methods for Comparative Effectiveness Research with Multiple...Kazuki Yoshida
My dissertation research (and a little more) as presented at the Study Design and Biostatistics Center, Department of Population Health Sciences, University of Utah.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
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Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).