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Univariate polytomous ordinal regres...

Batten, Dennis William.

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Univariate polytomous ordinal regression analysis with application to diabetic retinopathy data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Univariate polytomous ordinal regression analysis with application to diabetic retinopathy data./
Author:	Batten, Dennis William.
Description:	92 p.
Notes:	Adviser: B. C. Sutradhar.
Contained By:	Masters Abstracts International39-04.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ54859
ISBN:	0612548597

Univariate polytomous ordinal regression analysis with application to diabetic retinopathy data.
Batten, Dennis William.

Univariate polytomous ordinal regression analysis with application to diabetic retinopathy data. - 92 p.

Adviser: B. C. Sutradhar.

Thesis (M.A.S.)--Memorial University of Newfoundland (Canada), 2000.

There are many situations in practice where one is interested to find the regression effects of the covariates on polytomous responses. Furthermore, there are situations where polytomous responses are ordinal by nature. These types of data are commonly analyzed by exploiting the well-known probit and cumulative logit models. These methods, however, require the introduction of certain cut-points to distinguish ordered categories of the polytomous responses, and these cut-points are required to be estimated consistently, which may not be easily obtained. In the practicum, we use a recently developed non-cut-point based cumulative logit model to resolve this estimation problem. The regression analysis chosen in the practicum was motivated by a need for a refined analysis of a diabetes data set used in the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR). The practicum discusses the advantages and disadvantages of the existing as well as the new techniques. The non-cut-point based approach was found to give the best fit to the diabetes data, with easy interpretation of the regression estimates.

ISBN: 0612548597Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Univariate polytomous ordinal regression analysis with application to diabetic retinopathy data.
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035 $a (UnM)AAIMQ54859
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100 1 $a Batten, Dennis William. $3 1252751
245 1 0 $a Univariate polytomous ordinal regression analysis with application to diabetic retinopathy data.
300 $a 92 p.
500 $a Adviser: B. C. Sutradhar.
500 $a Source: Masters Abstracts International, Volume: 39-04, page: 1179.
502 $a Thesis (M.A.S.)--Memorial University of Newfoundland (Canada), 2000.
520 $a There are many situations in practice where one is interested to find the regression effects of the covariates on polytomous responses. Furthermore, there are situations where polytomous responses are ordinal by nature. These types of data are commonly analyzed by exploiting the well-known probit and cumulative logit models. These methods, however, require the introduction of certain cut-points to distinguish ordered categories of the polytomous responses, and these cut-points are required to be estimated consistently, which may not be easily obtained. In the practicum, we use a recently developed non-cut-point based cumulative logit model to resolve this estimation problem. The regression analysis chosen in the practicum was motivated by a need for a refined analysis of a diabetes data set used in the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR). The practicum discusses the advantages and disadvantages of the existing as well as the new techniques. The non-cut-point based approach was found to give the best fit to the diabetes data, with easy interpretation of the regression estimates.
590 $a School code: 0306.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Health Sciences, Ophthalmology. $3 1019445
650 4 $a Statistics. $3 517247
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690 $a 0381
690 $a 0463
710 2 0 $a Memorial University of Newfoundland (Canada). $3 1017906
773 0 $t Masters Abstracts International $g 39-04.
790 $a 0306
790 1 0 $a Sutradhar, B. C., $e advisor
791 $a M.A.S.
792 $a 2000
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ54859