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Applied multiple imputation = advant...

Kleinke, Kristian.

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Applied multiple imputation = advantages, pitfalls, new developments and applications in R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied multiple imputation/ by Kristian Kleinke ... [et al.].
其他題名:	advantages, pitfalls, new developments and applications in R /
其他作者:	Kleinke, Kristian.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xi, 292 p. :ill., digital ;24 cm.
內容註:	1 Introduction and Basic Concepts -- 2 Missing Data Mechanism and Ignorability -- 3 Missing Data Methods -- 4 Multiple Imputation: Theory -- 5 Multiple Imputation: Application -- 6 Multiple Imputation: New Developments -- A Appendices -- Index.
Contained By:	Springer eBooks
標題:	Multiple imputation (Statistics) -
電子資源:	https://doi.org/10.1007/978-3-030-38164-6
ISBN:	9783030381646

Applied multiple imputation = advantages, pitfalls, new developments and applications in R /
Applied multiple imputationadvantages, pitfalls, new developments and applications in R /[electronic resource] :by Kristian Kleinke ... [et al.]. - Cham :Springer International Publishing :2020. - xi, 292 p. :ill., digital ;24 cm. - Statistics for social and behavioral sciences,2199-7357. - Statistics for social and behavioral sciences..

1 Introduction and Basic Concepts -- 2 Missing Data Mechanism and Ignorability -- 3 Missing Data Methods -- 4 Multiple Imputation: Theory -- 5 Multiple Imputation: Application -- 6 Multiple Imputation: New Developments -- A Appendices -- Index.

This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros and cons of various techniques and concepts, including multiple imputation quality diagnostics, an important topic for practitioners. It also presents current research and new, practically relevant developments in the field, and demonstrates the use of recent multiple imputation techniques designed for situations where distributional assumptions of the classical multiple imputation solutions are violated. In addition, the book features numerous practical tutorials for widely used R software packages to generate multiple imputations (norm, pan and mice) The provided R code and data sets allow readers to reproduce all the examples and enhance their understanding of the procedures. This book is intended for social and health scientists and other quantitative researchers who analyze incompletely observed data sets, as well as master's and PhD students with a sound basic knowledge of statistics.

ISBN: 9783030381646

Standard No.: 10.1007/978-3-030-38164-6doiSubjects--Topical Terms:

648196
Multiple imputation (Statistics)

LC Class. No.: HA31.2

Dewey Class. No.: 519.282

Applied multiple imputation = advantages, pitfalls, new developments and applications in R /
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