Approximate nonparametric maximum likelihood for mixture models: a convex optimization approach to fitting arbitrary multivariate mixing distributions

DOI10.1016/j.csda.2018.01.006zbMath1469.62061OpenAlexW2790225006MaRDI QIDQ1662317

Lee H. Dicker, Long Feng

Publication date: 17 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2018.01.006

zbMATH Keywords

convex optimizationnonparametric maximum likelihoodmultivariate mixture modelsKiefer-Wolfowitz estimator

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric estimation (62G05) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (9)

A nonparametric empirical Bayes approach to large-scale multivariate regression ⋮ Poisson mean vector estimation with nonparametric maximum likelihood estimation and application to protein domain data ⋮ Uniform consistency in nonparametric mixture models ⋮ Likelihood Maximization and Moment Matching in Low <scp>SNR</scp> Gaussian Mixture Models ⋮ A Compound Decision Approach to Covariance Matrix Estimation ⋮ A Regression Modeling Approach to Structured Shrinkage Estimation ⋮ Simultaneous estimation of normal means with side information ⋮ High-dimensional linear discriminant analysis using nonparametric methods ⋮ Generalized maximum likelihood estimation of the mean of parameters of mixtures. With applications to sampling and to observational studies

Uses Software

• mixfdr
• Rmosek
• REBayes

Cites Work

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