Missing Data Methods
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Multiple Imputation Of Missing Data In Practice
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Author : Yulei He
language : en
Publisher: CRC Press
Release Date : 2021-11-19
Multiple Imputation Of Missing Data In Practice written by Yulei He and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-19 with Mathematics categories.
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 Methods
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Author : David M. Drukker
language : en
Publisher: Emerald Group Publishing
Release Date : 2011-11-23
Missing Data Methods written by David M. Drukker and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-23 with Business & Economics categories.
Contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; and, Consistent Estimation and Orthogonality.
Statistical Analysis With Missing Data
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Author : Roderick J. A. Little
language : en
Publisher:
Release Date : 1987-05-11
Statistical Analysis With Missing Data written by Roderick J. A. Little and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-05-11 with Mathematics categories.
Blending theory and application, this study reviews historical approaches to the subject and provides rigorous yet simple methods for multivariate analysis with missing values.
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
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.
Statistical Analysis With Missing Data
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Author : Roderick J. A. Little
language : en
Publisher: John Wiley & Sons
Release Date : 2014-08-25
Statistical Analysis With Missing Data written by Roderick J. A. Little 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 2014-08-25 with Mathematics categories.
* Emphasizes the latest trends in the field. * Includes a new chapter on evolving methods. * Provides updated or revised material in most of the chapters.
Applied Missing Data Analysis
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Author : Craig K. Enders
language : en
Publisher: Guilford Publications
Release Date : 2022-08-31
Applied Missing Data Analysis written by Craig K. Enders and has been published by Guilford Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-31 with Business & Economics categories.
Revised edition of the author's Applied missing data analysis, c2010.
Missing Data
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Author : John W. Graham
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-06-08
Missing Data written by John W. Graham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-08 with Social Science categories.
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 advancedreaders to expand their skill set.
Missing Data
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Author : Paul D. Allison
language : en
Publisher: SAGE Publications
Release Date : 2001-08-13
Missing Data written by Paul D. Allison and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-13 with Social Science categories.
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.
A Principal Component Missing Data Method For Multiple Regression Models
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Author : Robert Ernest Dear
language : en
Publisher:
Release Date : 1959
A Principal Component Missing Data Method For Multiple Regression Models written by Robert Ernest Dear and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1959 with Analysis of variance categories.