Semi-parametric estimation for conditional independence multivariate finite mixture models
Semi-parametric estimation for conditional independence multivariate finite mixture models
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DOI：
10.1214/15-ss108
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发表时间：
2015-01-01
影响因子：
3.3
通讯作者：
Levine, Michael
中科院分区：
文献类型：
--
作者：
Chauveau, Didier;Hunter, David R.;Levine, Michael
非参数多元有限混合模型的条件独立假设是众所周知的纵向数据随机效应模型条件独立假设的较弱形式，是统计文献中越来越多的理论和算法发展的主题。在对这些文献进行了调查之后，包括对最重要的可识别性结果的深入讨论，本文描述并扩展了用于估计这些模型中的参数的算法。该算法适用于任意数量的三个或更多维度的组件。它具有下降属性，可以轻松适应数据分组为条件独立变量块的情况。我们讨论了如何使该算法适应链接分量密度的各种位置尺度模型，甚至将其适应一类特定的单变量混合问题，其中假设分量是对称的。我们给出了算法的带宽选择过程。最后，我们使用模拟研究和两个心理测量数据集证明了我们算法的有效性。
The conditional independence assumption for nonparametric multivariate finite mixture models, a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data, is the subject of an increasing number of theoretical and algorithmic developments in the statistical literature. After presenting a survey of this literature, including an in-depth discussion of the all-important identifiability results, this article describes and extends an algorithm for estimation of the parameters in these models. The algorithm works for any number of components in three or more dimensions. It possesses a descent property and can be easily adapted to situations where the data are grouped in blocks of conditionally independent variables. We discuss how to adapt this algorithm to various location-scale models that link component densities, and we even adapt it to a particular class of univariate mixture problems in which the components are assumed symmetric. We give a bandwidth selection procedure for our algorithm. Finally, we demonstrate the effectiveness of our algorithm using a simulation study and two psychometric datasets.