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Multivariate longitudinal data analy...

Zhang, Xiang.

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Multivariate longitudinal data analysis with a family of covariance matrices.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multivariate longitudinal data analysis with a family of covariance matrices./
Author:	Zhang, Xiang.
Description:	146 p.
Notes:	Source: Dissertation Abstracts International, Volume: 57-02, Section: B, page: 1188.
Contained By:	Dissertation Abstracts International57-02B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9616425

Multivariate longitudinal data analysis with a family of covariance matrices.
Zhang, Xiang.

Multivariate longitudinal data analysis with a family of covariance matrices. - 146 p.

Source: Dissertation Abstracts International, Volume: 57-02, Section: B, page: 1188.

Thesis (Ph.D.)--University of California, Los Angeles, 1996.

In most longitudinal studies, multiple variables are repeatedly measured on each subject. We are often interested in the relationships among these repeatedly measured multiple variables. Structured covariance matrices are used in univariate repeated measurement analysis because the problems of inefficient estimation and invalid inference could be introduced by using unsuitable covariance structure. For example, AR-1, compound symmetric, and banded structures were considered by Jennrich and Schluchter (1986). The purpose of this paper is to provide several choices of structured covariance matrices for analyzing multivariate repeated measures and to provide an associated computational algorithm using Gibbs sampling. Estimations and hypothesis testing for the parameters of the mean and covariance structures are illustrated in an example of pediatric pain data.Subjects--Topical Terms:

517247
Statistics.

Multivariate longitudinal data analysis with a family of covariance matrices.
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008 130515s1996 ||||||||||||||||| ||eng d
035 $a (UMI)AAI9616425
035 $a AAI9616425
040 $a UMI $c UMI
100 1 $a Zhang, Xiang. $3 1279005
245 1 0 $a Multivariate longitudinal data analysis with a family of covariance matrices.
300 $a 146 p.
500 $a Source: Dissertation Abstracts International, Volume: 57-02, Section: B, page: 1188.
500 $a Chair: Robert E. Weiss.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 1996.
520 $a In most longitudinal studies, multiple variables are repeatedly measured on each subject. We are often interested in the relationships among these repeatedly measured multiple variables. Structured covariance matrices are used in univariate repeated measurement analysis because the problems of inefficient estimation and invalid inference could be introduced by using unsuitable covariance structure. For example, AR-1, compound symmetric, and banded structures were considered by Jennrich and Schluchter (1986). The purpose of this paper is to provide several choices of structured covariance matrices for analyzing multivariate repeated measures and to provide an associated computational algorithm using Gibbs sampling. Estimations and hypothesis testing for the parameters of the mean and covariance structures are illustrated in an example of pediatric pain data.
590 $a School code: 0031.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a University of California, Los Angeles. $3 626622
773 0 $t Dissertation Abstracts International $g 57-02B.
790 1 0 $a Weiss, Robert E., $e advisor
790 $a 0031
791 $a Ph.D.
792 $a 1996
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9616425