文章基本信息

标题：Maximum smoothed likelihood estimation for a class of semiparametric Pareto mixture densities
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作者：Huang, Mian ; Wang, Shaoli ; Wang, Hansheng 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2017
卷号：11
期号：1
页码：31-40
DOI：10.4310/SII.2018.v11.n1.a3
语种：English
出版社：International Press
摘要：Motivated by an analysis of Return On Equity (ROE) data, we propose a class of semiparametric mixture models. The proposed models have a symmetric nonparametric component and a parametric component of Pareto distribution with unknown parameters. However, situations with general parametric components other than Pareto distribution are also investigated. We prove that these mixture models are identifiable, and establish a novel estimation procedure via smoothed likelihood and profile-likelihood techniques. For ease of computation, we develop a new EM algorithm to facilitate the maximization problem. We show that this EM algorithm possesses the ascent property. A rule-of-thumb based procedure is proposed to select the bandwidth of the nonparametric component. Simulation studies demonstrate good performance of the proposed methodology. Furthermore, we analyze the ROE dataset which may consist of real and manipulated earnings. Our analysis reveals significant earning manipulation in the Chinese listed companies from a quantitative perspective using the proposed model.
关键词：semiparametric mixture models; EM algorithm; maximum smoothed likelihood estimation; Pareto mixture densities; bandwidth selection