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Unified ordinal regression: Model a...

Fu, Limin.

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Unified ordinal regression: Model assessment and semiparametric analysis.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Unified ordinal regression: Model assessment and semiparametric analysis./
作者:	Fu, Limin.
面頁冊數:	84 p.
附註:	Adviser: Douglas Simpson.
Contained By:	Dissertation Abstracts International61-01B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9955616
ISBN:	0599585528

Unified ordinal regression: Model assessment and semiparametric analysis.
Fu, Limin.

Unified ordinal regression: Model assessment and semiparametric analysis. - 84 p.

Adviser: Douglas Simpson.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000.

Various forms of logit model have been employed in the regression analysis of ordinal response data. In the first part of the thesis we adapt the theory and methodology of generalized estimating equations (GEE) and a binary coding of the ordinal response so that the various forms of logit model can be handled in a unified fashion. We develop Rao-type generalized score tests for model assessment within this framework.

ISBN: 0599585528Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Unified ordinal regression: Model assessment and semiparametric analysis.
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500 $a Source: Dissertation Abstracts International, Volume: 61-01, Section: B, page: 0350.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000.
520 $a Various forms of logit model have been employed in the regression analysis of ordinal response data. In the first part of the thesis we adapt the theory and methodology of generalized estimating equations (GEE) and a binary coding of the ordinal response so that the various forms of logit model can be handled in a unified fashion. We develop Rao-type generalized score tests for model assessment within this framework.
520 $a In the second part of the thesis we propose a class of latent structure models to the analysis of longitudinal ordinal data. We assume the observed ordinal scale is a manifestation of a latent continuous variable categorized by a set of unknown threshold values. The dependence among the repeated measurements for a subject is modeled through the latent continuous variable. Monte Carlo <italic> EM</italic> (MCEM) is applied to obtain the maximum likelihood estimates.
520 $a In the third part of the thesis we extend the generalized additive models proposed by Hastie and Tibshirani (1984) to multivariate data and propose a class of multivariate generalized additive models, which model the correlation structure of the components of a multivariate observation as well as the marginal means.
520 $a In the last part of the thesis we discuss the methods for handling censored data for both categorical and continuous responses. We develop <italic>EM </italic> algorithms for censored data in general, and also develop a weighted least squares algorithm for cumulative link models for ordinal responses.
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