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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.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Univariate polytomous ordinal regression analysis with application to diabetic retinopathy data./
作者:	Batten, Dennis William.
面頁冊數:	92 p.
附註:	Adviser: B. C. Sutradhar.
Contained By:	Masters Abstracts International39-04.
標題:	Biology, Biostatistics. -
電子資源:	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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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.
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