Multiple Imputation In Practice

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Multiple Imputation In Practice

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Author : Trivellore Raghunathan
language : en
Publisher: CRC Press
Release Date : 2018-07-20

Multiple Imputation In Practice written by Trivellore Raghunathan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-20 with Mathematics categories.

Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate both simple, and complex analyses. Version 0.3 of IVEware, the software developed by the University of Michigan, is used to illustrate analyses. IVEware can multiply impute missing values, analyze multiply imputed data sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows, Linux, and Mac, and with software packages like SAS, SPSS, Stata, and R, or as a stand-alone tool. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with examples of correct analysis techniques.

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)

Multiple Imputation And Its Application

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Author : James R. Carpenter
language : en
Publisher: John Wiley & Sons
Release Date : 2023-07-24

Multiple Imputation And Its Application written by James R. Carpenter 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 2023-07-24 with Medical categories.

Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to analyzing partially observed data In this comprehensively revised Second Edition of Multiple Imputation and its Application, a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings. With an accessible and carefully structured presentation aimed at quantitative researchers, Multiple Imputation and its Application is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find: A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets Careful discussion of key concepts A range of examples illustrating the key ideas Practical advice on using multiple imputation Exercises and examples designed for use in the classroom and/or private study Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic, Multiple Imputation and its Application will also earn a place in the libraries of graduate students undertaking quantitative analyses.

Multiple Imputation For Nonresponse In Surveys

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Author : Donald B. Rubin
language : en
Publisher: John Wiley & Sons
Release Date : 2004-06-09

Multiple Imputation For Nonresponse In Surveys written by Donald B. Rubin 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 2004-06-09 with Mathematics categories.

Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.

Proceedings Of The Section On Survey Research Methods

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Author : American Statistical Association. Survey Research Methods Section
language : en
Publisher:
Release Date : 1994

Proceedings Of The Section On Survey Research Methods written by American Statistical Association. Survey Research Methods Section and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Mathematical statistics categories.

Advances In Multilevel Modeling For Educational Research

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Author : Jeffrey R. Harring
language : en
Publisher: Emerald Group Publishing
Release Date : 2015-12-01

Advances In Multilevel Modeling For Educational Research written by Jeffrey R. Harring 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 2015-12-01 with Education categories.

This volume explores multilevel modeling in education and social sciences, highlighting best practices and innovations. Written by experts in statistics and psychology, it’s a key resource for researchers analyzing nested data structures in fields like education policy, psychology, and special education.

Incomplete Data In Sample Surveys Theory And Bibliographies

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Author : National Research Council (U.S.). Panel on Incomplete Data
language : en
Publisher:
Release Date : 1983

Incomplete Data In Sample Surveys Theory And Bibliographies written by National Research Council (U.S.). Panel on Incomplete Data and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Mathematics categories.

Multiple Imputation And Its Application

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Author : James Carpenter
language : en
Publisher: John Wiley & Sons
Release Date : 2012-12-19

Multiple Imputation And Its Application written by James Carpenter 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 2012-12-19 with Medical categories.

A practical guide to analysing partially observed data. Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods. This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials. Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation. Multiple Imputation and its Application is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application.

Multiple Imputation

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Author : Jörg Drechsler
language : en
Publisher:
Release Date : 2020

Multiple Imputation written by Jörg Drechsler and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Anthropology categories.

Missing values are a common problem in almost any data, whether collected through surveys, clinical trials, or epidemiological studies. This can lead to biased results if the missingness is not taken into account at the analysis stage. Multiple imputation is widely accepted as the most convenient strategy for dealing with item nonresponse in a proper way. With multiple imputation, missing values are imputed (i.e., replaced with plausible values given the observed data) more than once. The multiple copies allow accounting for the extra uncertainty from nonresponse using simple formulae (Rubin's combining rules) ensuring valid inferences based on the imputed data. This extra uncertainty is typically ignored with single imputation, resulting in estimated standard errors and confidence intervals that are too small and p values that are too significant.Following a general introduction, this entry starts by discussing the requirements for inference based on partially observed data. The inferential procedures for analyzing multiply imputed datasets are presented next, before illustrating the two main approaches for generating multiply imputed datasets: joint modeling and sequential regression. Various parametric and nonparametric imputation strategies are then discussed, followed by a simulation study, which illustrates how multiple imputation would be implemented in practice. Next, the entry discusses practical considerations, such as deciding which variables to include in the imputation models or picking the number of imputations. The entry concludes with a critical review of the limitations of multiple imputation, a discussion of potential alternatives, and an illustration of applications of the multiple framework beyond the nonresponse context.

Compstat

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Author :
language : en
Publisher:
Release Date : 2002

Compstat written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Mathematical statistics categories.

Proceedings of symposia held 1974-