Location-scale cumulative odds models for ordinal data: a generalized non-linear model approach.

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Cox C

doi

10.1002/sim.4780141105

subject

Has Abstract

pub_date

1995-06-15 00:00:00

pages

1191-203

issue

11

eissn

0277-6715

issn

1097-0258

journal_volume

14

pub_type

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