LOCATION-SCALE CUMULATIVE ODDS MODELS FOR ORDINAL DATA - A GENERALIZED NONLINEAR MODEL APPROACH

被引：50

作者：

COX, C

机构：

[1] Department of Biostatistics, University of Rochester, School of Medicine and Dentistry, Rochester, New York, 14642

来源：

STATISTICS IN MEDICINE | 1995年 / 14卷 / 11期

关键词：

D O I：

10.1002/sim.4780141105

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Proportional odds regression models for multinomial probabilities based on ordered categories have been generalized in two somewhat different directions. Models having scale as well as location parameters for adjustment of boundaries (on an unobservable, underlying continuum) between categories have been employed in the context of ROC analysis. Partial proportional odds models, having different regression adjustments for different multinomial categories, have also been proposed. This paper considers a synthesis and further generalization of these two families. With use of a number of examples, I discuss and illustrate properties of this extended family of models. Emphasis is on the computation of maximum likelihood estimates of parameters, asymptotic standard deviations, and goodness-of-fit statistics with use of non-linear regression programs in standard statistical software such as SAS.

引用

页码：1191 / 1203

页数：13

共 11 条

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