.png){kind=icon}
返回
Nonparametric Maximum Likelihood Methods for Binary Response Models With Random Coefficients
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2020.1802284.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The venerable method of maximum likelihood has found numerous recent applications innonparametricestimation of regression and shape constrained densities. For mixture models the nonparametric maximum likelihood estimator (NPMLE) of Kiefer and Wolfowitz plays a central role in recent developments of empirical Bayes methods. The NPMLE has also been proposed by Cosslett as an estimation method for single index linear models for binary response with random coefficients. However, computational difficulties have hindered its application. Combining recent developments in computational geometry and convex optimization, we develop a new approach to computation for such models that dramatically increases their computational tractability. Consistency of the method is established for an expanded profile likelihood formulation. The methods are evaluated in simulation experiments, compared to the deconvolution methods of Gautier and Kitamura and illustrated in an application to modal choice for journey-to-work data in the Washington DC area.for this article are available online.
Keyword:
Binary data analysis
Convex optimization
Empirical Bayes
Hyperplane arrangements
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
Gamma irradiation effects on cyanate ester/epoxy insulation materials for superconducting magnets
PREAI
分享收藏
查看更多内容