Unsupervised clustering using nonparametric finite mixture models

被引：0
|
作者：
Hunter, David R. R. [1 ]
机构：
[1] Penn State Univ, Dept Stat, University Pk, PA 16802 USA
来源：
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS | 2024年 / 16卷 / 01期
关键词：
EM algorithm; kernel density estimation; semiparametric mixture; MAXIMUM SMOOTHED LIKELIHOOD; SEMIPARAMETRIC ESTIMATION; COMPONENTS; NUMBER;
D O I：
10.1002/wics.1632
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
This article presents basic ideas of finite mixture models in which the number of components is known and the distributions comprising the components are not assumed to come from any parametrically specified family.This article is categorized under:Algorithms and Computational Methods > AlgorithmsStatistical Learning and Exploratory Methods of the Data Sciences > Clustering and ClassificationStatistical and Graphical Methods of Data Analysis > Nonparametric MethodsStatistical Models > Classification Models
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页数：11
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