Improved estimation in cumulative link models. Kosmidis, I. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1):169–196, January, 2014. ![Improved estimation in cumulative link models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex For the estimation of cumulative link models for ordinal data, the bias reducing adjusted score equations of Firth in 1993 are obtained, whose solution ensures an estimator with smaller asymptotic bias than the maximum likelihood estimator. Their form suggests a parameter-dependent adjustment of the multinomial counts, which in turn suggests the solution of the adjusted score equations through iterated maximum likelihood fits on adjusted counts, greatly facilitating implementation. Like the maximum likelihood estimator, the reduced bias estimator is found to respect the invariance properties that make cumulative link models a good choice for the analysis of categorical data. Its additional finiteness and optimal frequentist properties, along with the adequate behaviour of related asymptotic inferential procedures, make the reduced bias estimator attractive as a default choice for practical applications. Furthermore, the estimator proposed enjoys certain shrinkage properties that are defensible from an experimental point of view relating to the nature of ordinal data.
@article{kosmidis_improved_2014,
title = {Improved estimation in cumulative link models},
volume = {76},
issn = {13697412},
url = {http://doi.wiley.com/10.1111/rssb.12025},
doi = {10.1111/rssb.12025},
abstract = {For the estimation of cumulative link models for ordinal data, the bias reducing adjusted score equations of Firth in 1993 are obtained, whose solution ensures an estimator with smaller asymptotic bias than the maximum likelihood estimator. Their form suggests a parameter-dependent adjustment of the multinomial counts, which in turn suggests the solution of the adjusted score equations through iterated maximum likelihood fits on adjusted counts, greatly facilitating implementation. Like the maximum likelihood estimator, the reduced bias estimator is found to respect the invariance properties that make cumulative link models a good choice for the analysis of categorical data. Its additional finiteness and optimal frequentist properties, along with the adequate behaviour of related asymptotic inferential procedures, make the reduced bias estimator attractive as a default choice for practical applications. Furthermore, the estimator proposed enjoys certain shrinkage properties that are defensible from an experimental point of view relating to the nature of ordinal data.},
language = {en},
number = {1},
urldate = {2019-04-14TZ},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
author = {Kosmidis, Ioannis},
month = jan,
year = {2014},
pages = {169--196}
}
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