Nonparametric estimation of the mixing distribution in logistic regression mixed models with random intercepts and slopes

Mary Lesperance, Rabih Saab and John Neuhaus

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 211-219

Abstract: An algorithm that computes nonparametric maximum likelihood estimates of a mixing distribution for a logistic regression model containing random intercepts and slopes is proposed. The algorithm identifies mixing distribution support points as the maxima of the gradient function using a direct search method. The mixing proportions are then estimated through a quadratically convergent method. Two methods for computing the joint maximum likelihood estimates of the fixed effects parameters and the mixing distribution are compared. A simulation study demonstrates the performance of the algorithms and an example using National Basketball Association data is provided.

Keywords: Generalized linear mixed models with binary outcomes; Random effects; Direct search method; Nonparametric maximum likelihood estimation (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:211-219

DOI: 10.1016/j.csda.2013.05.014

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