國立高雄大學圖資館 |

Language: English

Back

Missing data analysis in practice

Raghunathan, Trivellore.

Missing data analysis in practice

Record Type:	Electronic resources : Monograph/item
Title/Author:	Missing data analysis in practiceTrivellore Raghunathan.
Author:	Raghunathan, Trivellore.
Published:	Boca Raton, FL :CRC Press, Taylor & Francis Group,c2016.
Description:	1 online resource (xix, 210 p.) :ill.
Notes:	"A Chapman & Hall book"--title page.
Subject:	Mathematical statistics.
Online resource:	https://www.taylorfrancis.com/books/9780429171673
ISBN:	9781482211931$q(electronic bk.)

Missing data analysis in practice
Raghunathan, Trivellore.

Missing data analysis in practice[electronic resource] /Trivellore Raghunathan. - 1st ed. - Boca Raton, FL :CRC Press, Taylor & Francis Group,c2016. - 1 online resource (xix, 210 p.) :ill. - Chapman & Hall/CRC interdisciplinary statistics series. - Interdisciplinary statistics..

"A Chapman & Hall book"--title page.

Includes bibliographical references (p. 187-203)

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: 9781482211931$q(electronic bk.)

LCCN: 2016302652Subjects--Topical Terms:

181877
Mathematical statistics.

LC Class. No.: QA276 / .R26 2015

Dewey Class. No.: 519.5

Missing data analysis in practice
LDR:02321cmm a2200313 a 4500 001 584325
005 20160907161409.0
006 m o d
007 cr cnu---unuuu
008 210204s2016 flua ob 000 0 eng d
010 $a 2016302652
020 $a 9781482211931$q(electronic bk.)
020 $a 9781482211924$q(hardback)
020 $a 1482211920$q(hardback)
035 $a fly19173142
040 $a BTCTA $b eng $c BTCTA $d BDX $d YDXCP $d OCLCO $d KSU $d OCLCQ $d DGU $d EYM $d OCLCF $d CDX $d OCLCQ $d DLC
041 0 $a eng
050 0 0 $a QA276 $b .R26 2015
082 0 4 $a 519.5 $2 23
100 1 $a Raghunathan, Trivellore. $3 875292
245 1 0 $a Missing data analysis in practice $h [electronic resource] / $c Trivellore Raghunathan.
250 $a 1st ed.
260 $a Boca Raton, FL : $b CRC Press, Taylor & Francis Group, $c c2016.
300 $a 1 online resource (xix, 210 p.) : $b ill.
490 1 $a Chapman & Hall/CRC interdisciplinary statistics series
500 $a "A Chapman & Hall book"--title page.
504 $a Includes bibliographical references (p. 187-203)
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.
588 $a Description based on print version record.
650 0 $a Mathematical statistics. $3 181877
650 0 $a Missing observations (Statistics) $3 182525
830 0 $a Interdisciplinary statistics. $3 242048
856 4 0 $u https://www.taylorfrancis.com/books/9780429171673