Missing Data Analysis in Practice

This book PDF is perfect for those who love Mathematics genre, written by Trivellore Raghunathan and published by CRC Press which was released on 28 October 2015 with total hardcover pages 227. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Missing Data Analysis in Practice books below.

Missing Data Analysis in Practice

Author	: Trivellore Raghunathan
File Size	: 47,8 Mb
Publisher	: CRC Press
Language	: English
Release Date	: 28 October 2015
ISBN	: 9781482211931
Pages	: 227 pages

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Missing Data Analysis in Practice by Trivellore Raghunathan Book PDF Summary

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes ea

Missing Data Analysis in Practice by Trivellore Raghunathan

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

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.

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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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Applied Missing Data Analysis by Craig K. Enders

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

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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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Missing Data by John W. Graham

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

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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

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

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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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