Log-ratio methods in mixture models for compositional data sets

被引:0
|
作者
Comas-Cufi, M. [1 ]
Martin-Fernandez, J. A. [1 ]
Mateu-Figueras, G. [1 ]
机构
[1] Univ Girona, Dept Comp Sci Appl Math & Stat, Campus Montilivi,P4, E-17071 Girona, Spain
关键词
Compositional data; Finite Mixture; Log ratio; Model-based clustering; Normal distribution; Orthonormal coordinates; Simplex; GENERALIZED LIOUVILLE DISTRIBUTIONS; MAXIMUM-LIKELIHOOD-ESTIMATION; STATISTICAL-ANALYSIS; TRANSFORMATIONS; CLASSIFICATION; PARAMETERS;
D O I
暂无
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
When traditional methods are applied to compositional data misleading and incoherent results could be obtained. Finite mixtures of multivariate distributions are becoming increasingly important nowadays. In this paper, traditional strategies to fit a mixture model into compositional data sets are revisited and the major difficulties are detailed. A new proposal using a mixture of distributions defined on orthonormal log-ratio coordinates is introduced. A real data set analysis is presented to illustrate and compare the different methodologies.
引用
收藏
页码:349 / 374
页数:26
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