Bayesian Approaches to Assessing the Parallel Lines Assumption in Cumulative Ordered Logit Models
Keyword(s):
We first review existing literature on cumulative logit models along with various ways to test the parallel lines assumption. Building on the traditional frequentist framework, we introduce a method of Bayesian assessment of null values to provide an alternative way to examine the parallel lines assumption using highest density intervals and regions of practical equivalence. Second, we propose a new hyperparameter cumulative logit model that can improve upon existing ones in addressing several challenges where traditional modeling techniques fail. We use two empirical examples from health research to showcase the Bayesian approaches.
2014 ◽
pp. 1408-1408
An extended cumulative logit model for detecting a shift in frequencies of sky-cloudiness conditions
2012 ◽
Vol 117
(D16)
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pp. n/a-n/a
2013 ◽
Vol 24
(6)
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pp. 1369-1384
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2005 ◽
Vol 72
(6)
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pp. 745-753
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2015 ◽
Vol 742
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pp. 445-448
2020 ◽