In:
Journal of the Royal Statistical Society Series C: Applied Statistics, Oxford University Press (OUP), Vol. 62, No. 2 ( 2013-03-01), p. 233-250
Abstract:
The proportional odds logistic regression model is widely used for relating an ordinal outcome to a set of covariates. When the number of outcome categories is relatively large, the sample size is relatively small and/or certain outcome categories are rare, maximum likelihood can yield biased estimates of the regression parameters. Firth and Kosmidis proposed a procedure to remove the leading term in the asymptotic bias of the maximum likelihood estimator. Their approach is most easily implemented for univariate outcomes. We derive a bias correction that exploits the proportionality between Poisson and multinomial likelihoods for multinomial regression models. Specifically, we describe a bias correction for the proportional odds logistic regression model, based on the likelihood from a collection of independent Poisson random variables whose means are constrained to sum to 1, that is straightforward to implement. The method proposed is motivated by a study of predictors of post-operative complications in patients undergoing colon or rectal surgery.
Type of Medium:
Online Resource
ISSN:
0035-9254
,
1467-9876
DOI:
10.1111/j.1467-9876.2012.01057.x
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2013
detail.hit.zdb_id:
204797-4
detail.hit.zdb_id:
1482300-7
detail.hit.zdb_id:
1476894-X
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