A Flexible Skewed Link Model for Ordinal Outcomes: An Application to Infertility
DOI:
https://doi.org/10.3889/oamjms.2020.4386Keywords:
Latent variable, cumulative regression, Markov chain Monte Carlo, ordinal data, skewed link functionAbstract
BACKGROUND: An important issue in modeling categorical response data is the choice of the links. The commonly used complementary log-log link is inclined to link misspecification due to its positive and fixed skewness parameter.
AIM: The objective of this paper is to introduce a flexible skewed link function for modeling ordinal data with some covariates.
METHODS: We introduce a flexible skewed link model for the cumulative ordinal regression model based on Chen model.
RESULTS: The main advantage suggested by the proposed links is the skewed link provide much more identifiable than the existing skewed links. The propriety of posterior distributions under proper and improper priors is explored in detail. An efficient Markov chain Monte Carlo algorithm is developed for sampling from the posterior distribution.
CONCLUSION: The proposed methodology is motivated and illustrated by ovary hyperstimulation syndrome data.
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Copyright (c) 2020 Mohammad Chehrazi, Seyed Hassan Saadat, Mahmoud Hajiahmadi, Mirko Spiroski (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
http://creativecommons.org/licenses/by-nc/4.0
