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

被引：4

作者：

Lesperance, Mary ^{[1
]}

Saab, Rabih ^{[1
]}

Neuhaus, John ^{[2
]}

机构：

[1] Univ Victoria, Dept Math & Stat, Victoria, BC V8W 3R4, Canada

[2] Univ Calif San Francisco, Dept Epidemiol & Biostat, San Francisco, CA 94143 USA

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2014年 / 71卷

基金：

美国国家卫生研究院; 加拿大自然科学与工程研究理事会;

关键词：

Generalized linear mixed models with binary outcomes; Random effects; Direct search method; Nonparametric maximum likelihood estimation; CONVERGENCE;

D O I：

10.1016/j.csda.2013.05.014

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：211 / 219

页数：9

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