Handbook of Missing Data Methodology

This book PDF is perfect for those who love Mathematics genre, written by Geert Molenberghs and published by CRC Press which was released on 06 November 2014 with total hardcover pages 590. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Handbook of Missing Data Methodology books below.

Handbook of Missing Data Methodology
Handbook of Missing Data Methodology
Author : Geert Molenberghs
File Size : 53,6 Mb
Publisher : CRC Press
Language : English
Release Date : 06 November 2014
ISBN : 9781439854624
Pages : 590 pages
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Handbook of Missing Data Methodology by Geert Molenberghs Book PDF Summary

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 Missing Data Methodology

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

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

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Missing Data

Missing Data by Paul D. Allison

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.

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Applied Missing Data Analysis

Applied Missing Data Analysis by Craig K. Enders

"The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on

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Statistical Methods for Handling Incomplete Data

Statistical Methods for Handling Incomplete Data by Jae Kwang Kim,Jun Shao

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

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Flexible Imputation of Missing Data Second Edition

Flexible Imputation of Missing Data Second Edition by Stef van Buuren

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 (

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Missing Data

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

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

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Statistical Analysis with Missing Data

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

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

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Applied Missing Data Analysis in the Health Sciences

Applied Missing Data Analysis in the Health Sciences by Xiao-Hua Zhou,Chuan Zhou,Danping Lui,Xaiobo Ding

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

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