Missing Data

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Flexible Imputation of Missing Data, Second Edition

Stef van Buuren
Flexible Imputation of Missing Data Second Edition Book PDF

Author : Stef van Buuren
Publisher : CRC Press
Page : 444 pages
File Size : 45,5 Mb
Release : 2018-07-17
Category : Mathematics
ISBN : 9780429960352

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Flexible Imputation of Missing Data, Second Edition by Stef van Buuren Pdf

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.

Missing Data

Paul D. Allison
Missing Data Book PDF

Author : Paul D. Allison
Publisher : SAGE Publications
Page : 100 pages
File Size : 51,5 Mb
Release : 2024-05-08
Category : Social Science
ISBN : 9781071962527

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Missing Data by Paul D. Allison Pdf

Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.

Missing Data

Patrick E. McKnight,Katherine M. McKnight,Souraya Sidani,Aurelio José Figueredo
Missing Data Book PDF

Author : Patrick E. McKnight,Katherine M. McKnight,Souraya Sidani,Aurelio José Figueredo
Publisher : Guilford Press
Page : 269 pages
File Size : 45,9 Mb
Release : 2007-03-28
Category : Social Science
ISBN : 9781606238202

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Missing Data by Patrick E. McKnight,Katherine M. McKnight,Souraya Sidani,Aurelio José Figueredo Pdf

While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study’s conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study results. When statistical remedies are needed--such as deletion procedures, augmentation methods, and single imputation and multiple imputation procedures--the book also explains how to make sound decisions about their use. Patrick E. McKnight's website offers a periodically updated annotated bibliography on missing data and links to other Web resources that address missing data.

Statistical Analysis with Missing Data

Roderick J. A. Little,Donald B. Rubin
Statistical Analysis with Missing Data Book PDF

Author : Roderick J. A. Little,Donald B. Rubin
Publisher : John Wiley & Sons
Page : 462 pages
File Size : 44,8 Mb
Release : 2019-04-23
Category : Mathematics
ISBN : 9780470526798

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Statistical Analysis with Missing Data by Roderick J. A. Little,Donald B. Rubin Pdf

An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

Applied Missing Data Analysis

Craig K. Enders
Applied Missing Data Analysis Book PDF

Author : Craig K. Enders
Publisher : Guilford Press
Page : 401 pages
File Size : 40,5 Mb
Release : 2010-04-23
Category : Psychology
ISBN : 9781606236390

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Applied Missing Data Analysis by Craig K. Enders Pdf

Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists. This book will appeal to researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science. It will also serve as a supplemental text for doctoral-level courses or seminars in advanced quantitative methods, survey analysis, longitudinal data analysis, and multilevel modeling, and as a primary text for doctoral-level courses or seminars in missing data.

Missing Data

John W. Graham
Missing Data Book PDF

Author : John W. Graham
Publisher : Springer Science & Business Media
Page : 303 pages
File Size : 42,6 Mb
Release : 2012-06-08
Category : Social Science
ISBN : 9781461440185

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Missing Data by John W. Graham Pdf

Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking. The objective of Missing Data: Analysis and Design is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power. Missing Data: Analysis and Design contains essential information for both beginners and advanced readers. For researchers with limited missing data analysis experience, this book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data; provides clear, step-by-step instructions for performing state-of-the-art multiple imputation analyses; and offers practical advice, based on over 20 years' experience, for avoiding and troubleshooting problems. For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effective planned missing data designs are provided. The author lays out missing data theory in a plain English style that is accessible and precise. Most analysis described in the book are conducted using the well-known statistical software packages SAS and SPSS, supplemented by Norm 2.03 and associated Java-based automation utilities. A related web site contains free downloads of the supplementary software, as well as sample empirical data sets and a variety of practical exercises described in the book to enhance and reinforce the reader’s learning experience. Missing Data: Analysis and Design and its web site work together to enable beginners to gain confidence in their ability to conduct missing data analysis, and more advanced readers to expand their skill set.

The Prevention and Treatment of Missing Data in Clinical Trials

National Research Council,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Handling Missing Data in Clinical Trials
The Prevention and Treatment of Missing Data in Clinical Trials Book PDF

Author : National Research Council,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Handling Missing Data in Clinical Trials
Publisher : National Academies Press
Page : 163 pages
File Size : 43,8 Mb
Release : 2010-12-21
Category : Medical
ISBN : 9780309186513

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The Prevention and Treatment of Missing Data in Clinical Trials by National Research Council,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Handling Missing Data in Clinical Trials Pdf

Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.

Machine Learning Pocket Reference

Matt Harrison
Machine Learning Pocket Reference Book PDF

Author : Matt Harrison
Publisher : "O'Reilly Media, Inc."
Page : 320 pages
File Size : 45,6 Mb
Release : 2019-08-27
Category : Computers
ISBN : 9781492047490

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Machine Learning Pocket Reference by Matt Harrison Pdf

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines

Multiple Imputation of Missing Data Using SAS

Patricia Berglund,Steven G. Heeringa
Multiple Imputation of Missing Data Using SAS Book PDF

Author : Patricia Berglund,Steven G. Heeringa
Publisher : SAS Institute
Page : 164 pages
File Size : 49,9 Mb
Release : 2014-07-01
Category : Computers
ISBN : 9781629592039

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Multiple Imputation of Missing Data Using SAS by Patricia Berglund,Steven G. Heeringa Pdf

Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.

Handbook of Missing Data Methodology

Geert Molenberghs,Garrett Fitzmaurice,Michael G. Kenward,Anastasios Tsiatis,Geert Verbeke
Handbook of Missing Data Methodology Book PDF

Author : Geert Molenberghs,Garrett Fitzmaurice,Michael G. Kenward,Anastasios Tsiatis,Geert Verbeke
Publisher : CRC Press
Page : 600 pages
File Size : 43,5 Mb
Release : 2014-11-06
Category : Mathematics
ISBN : 9781439854617

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Handbook of Missing Data Methodology by Geert Molenberghs,Garrett Fitzmaurice,Michael G. Kenward,Anastasios Tsiatis,Geert Verbeke Pdf

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

Survey Methodology and Missing Data

Seppo Laaksonen
Survey Methodology and Missing Data Book PDF

Author : Seppo Laaksonen
Publisher : Springer
Page : 224 pages
File Size : 53,9 Mb
Release : 2018-07-05
Category : Social Science
ISBN : 9783319790114

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Survey Methodology and Missing Data by Seppo Laaksonen Pdf

This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and analyzing a file once a survey has been conducted, it addresses fields as diverse as advanced weighting, editing, and imputation, which are not well-covered in corresponding survey books. Moreover, it presents numerous empirical examples from the author's extensive research experience, particularly real data sets from multinational surveys.

Semiparametric Theory and Missing Data

Anastasios Tsiatis
Semiparametric Theory and Missing Data Book PDF

Author : Anastasios Tsiatis
Publisher : Springer Science & Business Media
Page : 392 pages
File Size : 49,5 Mb
Release : 2007-01-15
Category : Mathematics
ISBN : 9780387373454

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Semiparametric Theory and Missing Data by Anastasios Tsiatis Pdf

This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

Multiple Imputation of Missing Data in Practice

Yulei He,Guangyu Zhang,Chiu-Hsieh Hsu
Multiple Imputation of Missing Data in Practice Book PDF

Author : Yulei He,Guangyu Zhang,Chiu-Hsieh Hsu
Publisher : CRC Press
Page : 419 pages
File Size : 51,8 Mb
Release : 2021-11-20
Category : Mathematics
ISBN : 9780429530975

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Multiple Imputation of Missing Data in Practice by Yulei He,Guangyu Zhang,Chiu-Hsieh Hsu Pdf

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book)

Missing Data in Clinical Studies

Geert Molenberghs,Michael Kenward
Missing Data in Clinical Studies Book PDF

Author : Geert Molenberghs,Michael Kenward
Publisher : John Wiley & Sons
Page : 526 pages
File Size : 47,5 Mb
Release : 2007-04-04
Category : Medical
ISBN : 0470510439

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Missing Data in Clinical Studies by Geert Molenberghs,Michael Kenward Pdf

Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described. Provides a practical guide to the analysis of clinical trials and related studies with missing data. Examines the problems caused by missing data, enabling a complete understanding of how to overcome them. Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism. Illustrated throughout with real-life case studies and worked examples from clinical trials. Details the use and implementation of the necessary statistical software, primarily SAS. Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman,Jennifer Hill
Data Analysis Using Regression and Multilevel Hierarchical Models Book PDF

Author : Andrew Gelman,Jennifer Hill
Publisher : Cambridge University Press
Page : 654 pages
File Size : 48,6 Mb
Release : 2007
Category : Mathematics
ISBN : 052168689X

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Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman,Jennifer Hill Pdf

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.