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Mean and median bias reduction : a concise review and application to adjacent-categories logit models
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Kosmidis, Ioannis (2023) Mean and median bias reduction : a concise review and application to adjacent-categories logit models. In: Kateri, M. and Moustaki, I., (eds.) Trends and Challenges in Categorical Data Analysis. Statistics for Social and Behavioral Sciences (SSBS) . Springer Nature, pp. 177-197. ISBN 9783031311857
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WRAP-mean-median-bias-reduction-concise-review-application-adjacent-categories-logit-models-Kosmidis-2023.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 7 April 2025. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (555Kb) |
Official URL: http://dx.doi.org/10.1007/978-3-031-31186-4_6
Abstract
The estimation of categorical response models using bias-reducing adjusted score equations has seen extensive theoretical research and applied use. The resulting estimates have been found to have superior frequentist properties to what maximum likelihood generally delivers and to be finite, even in cases where the maximum likelihood estimates are infinite. We briefly review mean and median bias reduction of maximum likelihood estimates via adjusted score equations in an illustration-driven way, and discuss their particular equivariance properties under parameter transformations. We then apply mean and median bias reduction to adjacent-categories logit models for ordinal responses. We show how ready bias reduction procedures for Poisson log-linear models can be used for mean and median bias reduction in adjacent-categories logit models with proportional odds and mean bias-reduced estimation in models with non-proportional odds. As in binomial logistic regression, the reduced-bias estimates are found to be finite even in cases where the maximum likelihood estimates are infinite. We also use the approximation of the bias of transformations of mean bias-reduced estimators to correct for the mean bias of model-based ordinal superiority measures. All developments are motivated and illustrated using real-data case studies and simulations.
Item Type: | Book Item | ||||||
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Subjects: | Q Science > QA Mathematics | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Library of Congress Subject Headings (LCSH): | Analysis of means, Mathematical statistics, Logistic regression analysis, Logits | ||||||
Series Name: | Statistics for Social and Behavioral Sciences (SSBS) | ||||||
Publisher: | Springer Nature | ||||||
ISBN: | 9783031311857 | ||||||
ISSN: | 2199-7357 | ||||||
Book Title: | Trends and Challenges in Categorical Data Analysis | ||||||
Editor: | Kateri, M. and Moustaki, I. | ||||||
Official Date: | 7 April 2023 | ||||||
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Page Range: | pp. 177-197 | ||||||
DOI: | 10.1007/978-3-031-31186-4_6 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 3 October 2023 | ||||||
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