Semiparametric mixtures of regressions

David R. Hunter; Derek S. Young

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Semiparametric mixtures of regressions

机译：回归的半参数混合

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摘要

We present an algorithm for estimating parameters in a mixture-of-regressions model in which the errors are assumed to be independent and identically distributed but no other assumption is made. This model is introduced as one of several recent generalizations of the standard fully parametric mixture of linear regressions in the literature. A sufficient condition for the identifiability of the parameters is stated and proved. Several different versions of the algorithm, including one that has a provable ascent property, are introduced. Numerical tests indicate the effectiveness of some of these algorithms.

机译：我们提出了一种算法，用于估计回归模型中的参数，其中误差被假定为独立且分布均匀，但未进行其他假设。该模型是文献中线性回归的标准完全参数混合的几种最新概括之一。陈述并证明了参数可识别性的充分条件。介绍了该算法的几种不同版本，包括具有可证明的上升特性的一种。数值测试表明了其中一些算法的有效性。

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来源
《Journal of nonparametric statistics》 |2012年第2期|p.19-38|共20页
作者
David R. Hunter; Derek S. Young;
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作者单位

Department of Statistics, Perm State University 326 Thomas Bldg, University Park, PA 16802, USA;

Department of Statistics, Perm State University 326 Thomas Bldg, University Park, PA 16802, USA;

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原文格式 PDF
正文语种 eng
中图分类
关键词
density estimation; EM algorithm; finite mixture model; identifiability;

机译：密度估计;EM算法;有限混合模型可识别性;

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Semiparametric mixtures of regressions

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