多元尖峰正态分布混合的简约性与参数估计

Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions.

作者信息

Browne Ryan P, Bagnato Luca, Punzo Antonio

机构信息

Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON Canada.

Department of Economic and Social Sciences, Catholic University of the Sacred Heart, Milano, Italy.

出版信息

Adv Data Anal Classif. 2024;18(3):597-625. doi: 10.1007/s11634-023-00558-2. Epub 2023 Sep 27.

DOI:10.1007/s11634-023-00558-2

PMID:39309701

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11411007/

Abstract

UNLABELLED

Mixtures of multivariate leptokurtic-normal distributions have been recently introduced in the clustering literature based on mixtures of elliptical heavy-tailed distributions. They have the advantage of having parameters directly related to the moments of practical interest. We derive two estimation procedures for these mixtures. The first one is based on the majorization-minimization algorithm, while the second is based on a fixed point approximation. Moreover, we introduce parsimonious forms of the considered mixtures and we use the illustrated estimation procedures to fit them. We use simulated and real data sets to investigate various aspects of the proposed models and algorithms.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11634-023-00558-2.

摘要

未标注

基于椭圆重尾分布的混合，多元尖峰正态分布的混合最近已被引入聚类文献中。它们具有参数直接与实际感兴趣的矩相关的优势。我们为这些混合推导了两种估计程序。第一种基于主元化-最小化算法，而第二种基于定点近似。此外，我们引入了所考虑混合的简约形式，并使用所说明的估计程序对其进行拟合。我们使用模拟和真实数据集来研究所提出模型和算法的各个方面。