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.

81 Identifying and predicting novel classes of long-term disease trajectories for patients with juvenile dermatomyositis using growth mixture models

2019 ◽  
Author(s):  
Claire Deakin ◽  
Charalampia Papadopoulou ◽  
Muthana Al Obaidi ◽  
Clarissa Pilkington ◽  
Lucy Wedderburn ◽  
...  
Keyword(s):  
Mixture Models ◽  
Juvenile Dermatomyositis ◽  
Growth Mixture Models ◽  
Growth Mixture

Transition through smoking stages in adolescents: an innovative perspective using growth mixture models

2021 ◽  
Author(s):  
Asghar MohammadpourAsl ◽  
Nazanin Masoudi ◽  
Nasrin Jafari ◽  
Samane Yaghoubi ◽  
Farzaneh Hamidi ◽  
...  
Keyword(s):  
Mixture Models ◽  
Growth Mixture Models ◽  
Growth Mixture

Modeling trend changes of mean annual exposure to PM2.5 particles in the Middle East countries via growth mixture models

2021 ◽  
Vol 14 (7) ◽  
Author(s):  
Gashtasb Mardani ◽  
Mahdiyeh Alikhani Faradonbeh ◽  
Zahra Fatahian Kelishadrokhi ◽  
Hadi Raeisi Shahraki
Keyword(s):  
Middle East ◽  
Mixture Models ◽  
Growth Mixture Models ◽  
Growth Mixture ◽  
Middle East Countries

scholarly journals Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions

Biostatistics ◽  
2010 ◽  
Vol 11 (2) ◽  
pp. 317-336 ◽  
Author(s):  
Sylvia Frühwirth-Schnatter ◽  
Saumyadipta Pyne
Keyword(s):  
Bayesian Inference ◽  
Mixture Models ◽  
Random Effects ◽  
Data Augmentation ◽  
Heterogeneous Data ◽  
High Dimensional ◽  
Finite Mixtures ◽  
Truncated Normal ◽  
T Distribution ◽  
Skew Normal

Abstract Skew-normal and skew-t distributions have proved to be useful for capturing skewness and kurtosis in data directly without transformation. Recently, finite mixtures of such distributions have been considered as a more general tool for handling heterogeneous data involving asymmetric behaviors across subpopulations. We consider such mixture models for both univariate as well as multivariate data. This allows robust modeling of high-dimensional multimodal and asymmetric data generated by popular biotechnological platforms such as flow cytometry. We develop Bayesian inference based on data augmentation and Markov chain Monte Carlo (MCMC) sampling. In addition to the latent allocations, data augmentation is based on a stochastic representation of the skew-normal distribution in terms of a random-effects model with truncated normal random effects. For finite mixtures of skew normals, this leads to a Gibbs sampling scheme that draws from standard densities only. This MCMC scheme is extended to mixtures of skew-t distributions based on representing the skew-t distribution as a scale mixture of skew normals. As an important application of our new method, we demonstrate how it provides a new computational framework for automated analysis of high-dimensional flow cytometric data. Using multivariate skew-normal and skew-t mixture models, we could model non-Gaussian cell populations rigorously and directly without transformation or projection to lower dimensions.

scholarly journals Class enumeration false positive in skew-t family of continuous growth mixture models

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0231525
Author(s):  
Kiero Guerra-Peña ◽  
Zoilo Emilio García-Batista ◽  
Sarah Depaoli ◽  
Luis Eduardo Garrido
Keyword(s):  
Mixture Models ◽  
False Positive ◽  
Continuous Growth ◽  
Growth Mixture Models ◽  
Class Enumeration ◽  
Growth Mixture
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