Analysis of Antibody Data Using Skew-Normal and Skew-t Mixture Models

被引：1

作者：

Domingues, Tiago Dias ^{[1
]}

Mourino, Helena ^{[2
]}

Sepulveda, Nuno ^{[1
,3
]}

机构：

[1] Univ Lisbon, Fac Ciencias, CEAUL, Lisbon, Portugal

[2] Univ Lisbon, Fac Ciencias, CMAFcIO, Lisbon, Portugal

[3] Warsaw Univ Technol, Fac Math & Informat Sci, Warsaw, Poland

来源：

REVSTAT-STATISTICAL JOURNAL | 2024年 / 22卷 / 01期

关键词：

finite mixture models; Skew-Normal; Skew-t; seropositivity; FINITE MIXTURE; SYMMETRIC DISTRIBUTIONS; INFORMATION CRITERION; FISHER INFORMATION; SCALE MIXTURES; IDENTIFICATION; POPULATION; SELECTION; NUMBER; ASSAY;

D O I：

10.57805/revstat.v22i1.455

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：

Gaussian mixture models, which assume a Normal distribution for each component, are popular in antibody (or serological) data analysis to help determining antibody-positive and antibody- negative individuals. In this work, we advocate using finite mixture models based on Skew-Normal and Skew-t distributions for serological data analysis. These flexible mixing distributions have the advantage of describing right and left asymmetry often observed in the distributions of known antibody-negative and antibody-positive individuals, respectively. We illustrate the application of these alternative mixture models in a data set on the role of human herpesviruses in the Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

引用

页码：111 / 132

页数：22

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