Empirical likelihood inference for monotone index model

Otsu, Taisuke ORCID: 0000-0002-2307-143X, Takahata, Keisuke and Xu, Mengshan (2023) Empirical likelihood inference for monotone index model. Japanese Journal of Statistics and Data Science, 6 (1). pp. 103-114. ISSN 2520-8756

Text (Otsu_empirical-likelihood-inference--published) - Published Version Available under License Creative Commons Attribution. Download (265kB)

• Author
• Scopus publication

Abstract

This paper proposes an empirical likelihood inference method for monotone index models. We construct the empirical likelihood function based on a modified score function developed by Balabdaoui et al. (Scand J Stat 46:517–544, 2019), where the monotone link function is estimated by isotonic regression. It is shown that the empirical likelihood ratio statistic converges to a weighted chi-squared distribution. We suggest inference procedures based on an adjusted empirical likelihood statistic that is asymptotically pivotal, and a bootstrap calibration with recentering. A simulation study illustrates usefulness of the proposed inference methods.

Item Type:	Article
Official URL:	https://www.springer.com/journal/42081
Additional Information:	© 2023 The Authors
Divisions:	Economics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	07 Feb 2023 12:03
Last Modified:	12 Dec 2024 03:34
URI:	http://eprints.lse.ac.uk/id/eprint/118123

Actions (login required)

View Item