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Missing data analysis in practice /

Raghunathan, Trivellore.

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Missing data analysis in practice /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Missing data analysis in practice // Trivellore Raghunathan.
Author:	Raghunathan, Trivellore.
Published:	Boca Raton, FL :CRC Press/Taylor & Francis Group, : c2016.,
Description:	xix, 210 p. :ill. ;25 cm.
[NT 15003449]:	Basic concepts -- Weighting methods -- Imputation -- Multiple imputation -- Regression analysis -- Longitudinal analysis with missing values -- Nonignorable missing data mechanisms -- Other applications -- Other topics.
Subject:	Mathematical statistics. -
ISBN:	9781482211924

Missing data analysis in practice /
Raghunathan, Trivellore.

Missing data analysis in practice /Trivellore Raghunathan. - Boca Raton, FL :CRC Press/Taylor & Francis Group,c2016. - xix, 210 p. :ill. ;25 cm. - Chapman & Hall/CRC interdisciplinary statistics series. - Interdisciplinary statistics..

Includes bibliographical references (p. 187-203) and index.

Basic concepts -- Weighting methods -- Imputation -- Multiple imputation -- Regression analysis -- Longitudinal analysis with missing values -- Nonignorable missing data mechanisms -- Other applications -- Other topics.

"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 easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online. The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference"--

ISBN: 9781482211924UK49.99

LCCN: 2016302652Subjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276 / .R26 2016

Dewey Class. No.: 519.5

Missing data analysis in practice /
LDR:02082nam a2200253 a 4500 001 2017464
003 DLC
005 20160714172437.0
008 161003s2016 flua b 001 0 eng d
010 $a 2016302652
020 $a 9781482211924 $q (hardcover) : $c UK49.99
020 $a 1482211920 $q (hardcover)
020 $z 9781482211931 $q (PDF ebook)
035 $a (OCoLC)ocn909324190
040 $a BTCTA $b eng $c BTCTA $d BDX $d YDXCP $d SA$ $d OCLCO $d KSU $d OCLCQ $d DGU $d EYM $d OCLCF $d CDX $d OCLCQ $d DLC
042 $a lccopycat
050 0 0 $a QA276 $b .R26 2016
082 0 4 $a 519.5 $2 23
100 1 $a Raghunathan, Trivellore. $3 2184938
245 1 0 $a Missing data analysis in practice / $c Trivellore Raghunathan.
260 $a Boca Raton, FL : $b CRC Press/Taylor & Francis Group, $c c2016.
300 $a xix, 210 p. : $b ill. ; $c 25 cm.
490 1 $a Chapman & Hall/CRC interdisciplinary statistics series
504 $a Includes bibliographical references (p. 187-203) and index.
505 0 $a Basic concepts -- Weighting methods -- Imputation -- Multiple imputation -- Regression analysis -- Longitudinal analysis with missing values -- Nonignorable missing data mechanisms -- Other applications -- Other topics.
520 $a "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 easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online. The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference"-- $c Provided by publisher.
650 0 $a Mathematical statistics. $3 516858
650 0 $a Missing observations (Statistics) $3 566005
830 0 $a Interdisciplinary statistics. $3 626308