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Nonparametric maximum likelihood methods for binary response models with random coefficients

Banff International Research Station for Mathematical Innovation and Discovery

Single index linear models for binary response with random coefficients have been extensively employed in many settings under various parametric specifications of the distribution of the random coefficients. Nonparametric maximum likelihood estimation (NPMLE) as proposed by Kiefer and Wolfowitz (1956) in contrast, has received less attention in applied work due primarily to computational difficulties. We propose a new approach to computation of NPMLEs for binary response models that significantly increase their computational tractability thereby facilitating greater flexibility in applications. Our approach, which relies on recent developments involving the geometry of hyperplane arrangements by Rada and Ä erný (2018), is contrasted with the recently proposed deconvolution method of Gautier and Kitamura (2013).

Mathematics; Statistics

video/mp4

Author affiliation: University College London

2019-10-06

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/71833

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/