Small sample inference for probabilistic index models

Authors

Gustavo Amorim, Ghent University, Belgium
Olivier Thas, University of WollongongFollow
Karel K. Vermeulen, Ghent University, Belgium
Stijn Vansteelandt, Ghent University, Belgium
Jan De Neve, Ghent University, BelgiumFollow

RIS ID

124995

Publication Details

Amorim, G., Thas, O., Vermeulen, K., Vansteelandt, S. & De Neve, J. (2018). Small sample inference for probabilistic index models. Computational Statistics and Data Analysis, 121 137-148.

Abstract

Probabilistic index models may be used to generate classical and new rank tests, with the additional advantage of supplementing them with interpretable effect size measures. The popularity of rank tests for small sample inference makes probabilistic index models also natural candidates for small sample studies. However, at present, inference for such models relies on asymptotic theory that can deliver poor approximations of the sampling distribution if the sample size is rather small. A bias-reduced version of the bootstrap and adjusted jackknife empirical likelihood are explored. It is shown that their application leads to drastic improvements in small sample inference for probabilistic index models, justifying the use of such models for reliable and informative statistical inference in small sample studies.