Nonparametric hierarchical mixture models based on asymmetric Gaussian distribution

2020 ◽  
Vol 106 ◽  
pp. 102829
Author(s):  
Ziyang Song ◽  
Samr Ali ◽  
Nizar Bouguila ◽  
Wentao Fan
Keyword(s):  
Mixture Models ◽  
Gaussian Distribution ◽  
Hierarchical Mixture Models

scholarly journals Hierarchical Mixture Models for Expression Profiles

2009 ◽  
pp. 40-52 ◽  
Author(s):  
Michael A. Newton ◽  
Ping Wang ◽  
Christina Kendziorski ◽  
Marina Vannucci
Keyword(s):  
Mixture Models ◽  
Expression Profiles ◽  
Hierarchical Mixture Models

Hierarchical mixture models for biclustering in microarray data

2011 ◽  
Vol 11 (6) ◽  
pp. 489-505 ◽  
Author(s):  
F Martella ◽  
M Alfò ◽  
M Vichi
Keyword(s):  
Mixture Models ◽  
Microarray Data ◽  
Hierarchical Mixture Models

scholarly journals Effect-Size Estimation Using Semiparametric Hierarchical Mixture Models in Disease-Association Studies with Neuroimaging Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ryo Emoto ◽  
Atsushi Kawaguchi ◽  
Kunihiko Takahashi ◽  
Shigeyuki Matsui
Keyword(s):  
Size Distribution ◽  
Mixture Models ◽  
Effect Size ◽  
Association Studies ◽  
Disease Association ◽  
Spatial Dependency ◽  
Size Estimation ◽  
Neuroimaging Data ◽  
Hierarchical Mixture Models ◽  
Effect Size Distribution

In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or effect sizes for individual brain areas. In this paper, we propose a model-based framework for voxel-based inferences under spatial dependency in neuroimaging data. Specifically, we employ hierarchical mixture models with a hidden Markov random field structure to incorporate the spatial dependency between voxels. A nonparametric specification is proposed for the effect size distribution to flexibly estimate the underlying effect size distribution. Simulation experiments demonstrate that compared with a naive estimation method, the proposed methods can substantially reduce the selection bias in the effect size estimates of the selected voxels with the greatest observed associations. An application to neuroimaging data from an Alzheimer’s disease study is provided.

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