Regularization in Finite Mixture of Regression Models with Diverging Number of Parameters

DOI10.1111/biom.12020zbMath1273.62254OpenAlexW1482041358WikidataQ44604542 ScholiaQ44604542MaRDI QIDQ2846451

Abbas Khalili, Shili Lin

Publication date: 5 September 2013

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.12020

zbMATH Keywords

regularizationEM algorithmlassoParkinson's diseasetelemonitoringscadbreast cancer prognosiscell nuclear features

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

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Cites Work

• Unnamed Item
• Nearly unbiased variable selection under minimax concave penalty
• \(\ell_{1}\)-penalization for mixture regression models
• Consistent estimation of a mixing distribution
• Identifiability of models for clusterwise linear regression
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Nonconcave penalized likelihood with a diverging number of parameters.
• High-dimensional generalized linear models and the lasso
• A Statistical Framework for eQTL Mapping Using RNA-seq Data
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Variable Selection in Finite Mixture of Regression Models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regularization and Variable Selection Via the Elastic Net

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