Handbook Of Missing Data Methodology
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Handbook Of Missing Data Methodology
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Author : Geert Molenberghs
language : en
Publisher: CRC Press
Release Date : 2014-11-06
Handbook Of Missing Data Methodology written by Geert Molenberghs and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-06 with Mathematics categories.
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 t
Handbook Of Statistical Data Editing And Imputation
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Author : Ton de Waal
language : en
Publisher: John Wiley & Sons
Release Date : 2011-03-22
Handbook Of Statistical Data Editing And Imputation written by Ton de Waal and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-22 with Mathematics categories.
A practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques Collected survey data are vulnerable to error. In particular, the data collection stage is a potential source of errors and missing values. As a result, the important role of statistical data editing, and the amount of resources involved, has motivated considerable research efforts to enhance the efficiency and effectiveness of this process. Handbook of Statistical Data Editing and Imputation equips readers with the essential statistical procedures for detecting and correcting inconsistencies and filling in missing values with estimates. The authors supply an easily accessible treatment of the existing methodology in this field, featuring an overview of common errors encountered in practice and techniques for resolving these issues. The book begins with an overview of methods and strategies for statistical data editing and imputation. Subsequent chapters provide detailed treatment of the central theoretical methods and modern applications, with topics of coverage including: Localization of errors in continuous data, with an outline of selective editing strategies, automatic editing for systematic and random errors, and other relevant state-of-the-art methods Extensions of automatic editing to categorical data and integer data The basic framework for imputation, with a breakdown of key methods and models and a comparison of imputation with the weighting approach to correct for missing values More advanced imputation methods, including imputation under edit restraints Throughout the book, the treatment of each topic is presented in a uniform fashion. Following an introduction, each chapter presents the key theories and formulas underlying the topic and then illustrates common applications. The discussion concludes with a summary of the main concepts and a real-world example that incorporates realistic data along with professional insight into common challenges and best practices. Handbook of Statistical Data Editing and Imputation is an essential reference for survey researchers working in the fields of business, economics, government, and the social sciences who gather, analyze, and draw results from data. It is also a suitable supplement for courses on survey methods at the upper-undergraduate and graduate levels.
Handbook Of Missing Data Methodology
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Author : Geert Molenberghs
language : en
Publisher: CRC Press
Release Date : 2014-11-06
Handbook Of Missing Data Methodology written by Geert Molenberghs and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-06 with Mathematics categories.
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.
Applied Missing Data Analysis In The Health Sciences
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Author : Xiao-Hua Zhou
language : en
Publisher: John Wiley & Sons
Release Date : 2014-06-30
Applied Missing Data Analysis In The Health Sciences written by Xiao-Hua Zhou and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-30 with Medical categories.
A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various modern statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference methods and the field of diagnostic medicine. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into traditional techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book’s subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: Multiple data sets that can be replicated using the SAS®, Stata®, R, and WinBUGS software packages Numerous examples of case studies in the field of biostatistics to illustrate real-world scenarios and demonstrate applications of discussed methodologies Detailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.
Statistical Methods For Handling Incomplete Data
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Author : Jae Kwang Kim
language : en
Publisher: CRC Press
Release Date : 2013-07-23
Statistical Methods For Handling Incomplete Data written by Jae Kwang Kim and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-23 with Mathematics categories.
Along with many examples, this text covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. It presents a thorough treatment of statistical theories of likelihood-based inference with missing data. It also discusses numerous computational techniques and theories on imputation and extensively covers methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching. Some of the research ideas introduced can be developed further for specific applications.
Missing Data
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Author : Paul D. Allison
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 2001-08-13
Missing Data written by Paul D. Allison and has been published by SAGE Publications, Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-13 with Mathematics categories.
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 relied 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
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Author : Patrick E. McKnight
language : en
Publisher: Guilford Press
Release Date : 2007-03-28
Missing Data written by Patrick E. McKnight and has been published by Guilford Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-28 with Social Science categories.
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.
Preventing And Treating Missing Data In Longitudinal Clinical Trials
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Author : Craig Mallinckrodt
language : en
Publisher:
Release Date : 2013
Preventing And Treating Missing Data In Longitudinal Clinical Trials written by Craig Mallinckrodt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Clinical trials categories.
Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset"
International Handbook Of Survey Methodology
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Author : Edith D. de Leeuw
language : en
Publisher: Routledge
Release Date : 2012-10-12
International Handbook Of Survey Methodology written by Edith D. de Leeuw and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-12 with Psychology categories.
Taking into account both traditional and emerging modes, this comprehensive new Handbook covers all major methodological and statistical issues in designing and analyzing surveys. With contributions from the world's leading survey methodologists and statisticians, this invaluable new resource provides guidance on collecting survey data and creating meaningful results. Featuring examples from a variety of countries, the book reviews such things as how to deal with sample designs, write survey questions, and collect data on the Internet. A thorough review of the procedures associated with multiple modes of collecting sample survey information and applying that combination of methods that fit the situation best is included. The International Handbook of Survey Methodology opens with the foundations of survey design, ranging from sources of error, to ethical issues. This is followed by a section on design that reviews sampling challenges and tips on writing and testing questions for multiple methods. Part three focuses on data collection, from face-to-face interviews, to Internet and interactive voice response, to special challenges involved in mixing these modes within one survey. Analyzing data from both simple and complex surveys is then explored, as well as procedures for adjusting data. The book concludes with a discussion of maintaining quality. Intended for advanced students and researchers in the behavioral, social, and health sciences, this "must have" resource will appeal to those interested in conducting or using survey data from anywhere in the world, especially those interested in comparing results across countries. The book also serves as a state-of-the-art text for graduate level courses and seminars on survey methodology. A companion website contains additional readings and examples.
A Handbook Of Statistical Analyses Using R Third Edition
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Author : Torsten Hothorn
language : en
Publisher: CRC Press
Release Date : 2014-06-25
A Handbook Of Statistical Analyses Using R Third Edition written by Torsten Hothorn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-25 with Mathematics categories.
Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis. New to the Third Edition Three new chapters on quantile regression, missing values, and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses Whether you’re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.