Analysis of antibody data using Finite Mixture Models based on Scale Mixtures of Skew-Normal distributions

Abstract

Finite mixture models have been widely used in antibody (or serological) data analysis in order to help classifying individuals into either antibody-positive or antibody-negative. The most popular models are the so-called Gaussian mixture models which assume a Normal distribution for each component of a mixture. In this work, we propose the use of finite mixture models based on a flexible class of scale mixtures of Skew-Normal distributions for serological data analysis. These distributions are sufficiently flexible to describe right and left asymmetry often observed in the distributions associated with hypothetical antibody-negative and antibody-positive individuals, respectively. We illustrate the advantage of these alternative mixture models with a data set of 406 individuals in which antibodies against six different human herpesviruses were measured in the context of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.