Gaussian Scale Mixture Models for Robust Linear Multivariate Regression with Missing Data

2015 ◽  
Vol 45 (3) ◽  
pp. 791-813 ◽  
Author(s):  
Juha Ala-Luhtala ◽  
Robert Piché
Keyword(s):  
Missing Data ◽  
Mixture Models ◽  
Multivariate Regression ◽  
Scale Mixture ◽  
Gaussian Scale Mixture

scholarly journals Speech Enhancement Using Gaussian Scale Mixture Models

2010 ◽  
Vol 18 (6) ◽  
pp. 1127-1136 ◽  
Author(s):  
Jiucang Hao ◽  
Te-Won Lee ◽  
Terrence J Sejnowski
Keyword(s):  
Mixture Models ◽  
Speech Enhancement ◽  
Scale Mixture ◽  
Gaussian Scale Mixture

scholarly journals MGMM: An R Package for fitting Gaussian Mixture Models on Incomplete Data

2019 ◽  
Author(s):  
Zachary R. McCaw ◽  
Hanna Julienne ◽  
Hugues Aschard
Keyword(s):  
Missing Data ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Model Fitting ◽  
Simulated Data ◽  
R Package ◽  
Gaussian Mixture ◽  
Parameter Estimates ◽  
Cluster Assignment ◽  
Underlying Distribution

AbstractAlthough missing data are prevalent in applications, existing implementations of Gaussian mixture models (GMMs) require complete data. Standard practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates. Here we present MGMM, an R package for fitting GMMs in the presence of missing data. Using three case studies on real and simulated data sets, we demonstrate that, when the underlying distribution is near-to a GMM, MGMM is more effective at recovering the true cluster assignments than state of the art imputation followed by standard GMM. Moreover, MGMM provides an accurate assessment of cluster assignment uncertainty even when the generative distribution is not a GMM. This assessment may be used to identify unassignable observations. MGMM is available as an R package on CRAN: https://CRAN.R-project.org/package=MGMM.

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