Shape Mixture Models Based on Multivariate Extended Skew Normal Distributions

2017 ◽  
pp. 273-286
Author(s):  
Weizhong Tian ◽  
Tonghui Wang ◽  
Fengrong Wei ◽  
Fang Dai
Keyword(s):  
Mixture Models ◽  
Normal Distributions ◽  
Skew Normal Distributions ◽  
Skew Normal

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

2021 ◽  
Author(s):  
Tiago Dias Domingues ◽  
Helena Mouriño ◽  
Nuno Sepúlveda
Keyword(s):  
Data Analysis ◽  
Mixture Models ◽  
Finite Mixture Models ◽  
Finite Mixture ◽  
Data Set ◽  
Normal Distributions ◽  
Scale Mixtures ◽  
Skew Normal Distributions ◽  
Serological Data ◽  
Skew Normal

AbstractFinite 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.

scholarly journals Multivariate extremes of generalized skew-normal distributions

2009 ◽  
Vol 79 (4) ◽  
pp. 525-533 ◽  
Author(s):  
Natalia Lysenko ◽  
Parthanil Roy ◽  
Rolf Waeber
Keyword(s):  
Multivariate Extremes ◽  
Normal Distributions ◽  
Skew Normal Distributions ◽  
Skew Normal

Finite mixtures of multivariate scale-shape mixtures of skew-normal distributions

2018 ◽  
Vol 61 (6) ◽  
pp. 2643-2670
Author(s):  
Wan-Lun Wang ◽  
Ahad Jamalizadeh ◽  
Tsung-I Lin
Keyword(s):  
Finite Mixtures ◽  
Normal Distributions ◽  
Skew Normal Distributions ◽  
Skew Normal

Scale Mixtures of Skew-Normal Distributions

2018 ◽  
pp. 15-36
Author(s):  
Víctor Hugo Lachos Dávila ◽  
Celso Rômulo Barbosa Cabral ◽  
Camila Borelli Zeller
Keyword(s):  
Normal Distributions ◽  
Scale Mixtures ◽  
Skew Normal Distributions ◽  
Skew Normal

Moments and quadratic forms of matrix variate skew normal distributions

2015 ◽  
Vol 45 (3) ◽  
pp. 794-803
Author(s):  
Shimin Zheng ◽  
Jeff Knisley ◽  
Kesheng Wang
Keyword(s):  
Quadratic Forms ◽  
Normal Distributions ◽  
Skew Normal Distributions ◽  
Skew Normal

scholarly journals A Comparison of Different Nonnormal Distributions in Growth Mixture Models

2019 ◽  
Vol 79 (3) ◽  
pp. 577-597
Author(s):  
Sookyoung Son ◽  
Hyunjung Lee ◽  
Yoona Jang ◽  
Junyeong Yang ◽  
Sehee Hong
Keyword(s):  
Mixture Models ◽  
Outcome Variable ◽  
Normal Distributions ◽  
Nonnormal Distributions ◽  
Growth Mixture Models ◽  
Time Points ◽  
Growth Mixture ◽  
Distal Outcome ◽  
T Distribution ◽  
Skew Normal

The purpose of the present study is to compare nonnormal distributions (i.e., t, skew-normal, skew- t with equal skew and skew- t with unequal skew) in growth mixture models (GMMs) based on diverse conditions of a number of time points, sample sizes, and skewness for intercepts. To carry out this research, two simulation studies were conducted with two different models: an unconditional GMM and a GMM with a continuous distal outcome variable. For the simulation, data were generated under the conditions of a different number of time points (4, 8), sample size (300, 800, 1,500), and skewness for intercept (1.2, 2, 4). Results demonstrate that it is not appropriate to fit nonnormal data to normal, t, or skew-normal distributions other than the skew- t distribution. It was also found that if there is skewness over time, it is necessary to model skewness in the slope as well.

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