A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming

DOI10.1080/10618600.2019.1689985OpenAlexW3106246664WikidataQ111149822 ScholiaQ111149822MaRDI QIDQ89288

Peter Carbonetto, Matthew Stephens, Youngseok Kim, Mihai Anitescu, Youngseok Kim, Peter Carbonetto, Matthew Stephens, Mihai Anitescu

Publication date: 8 January 2020

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7986967

zbMATH Keywords

convex optimizationsequential quadratic programmingmixture modelsactive set methodsnonparametric empirical Bayesrank-revealing QR decomposition

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Rejoinder: Confidence Intervals for Nonparametric Empirical Bayes Analysis ⋮ Prices, profits, proxies, and production ⋮ Unnamed Item ⋮ mixsqp

Uses Software

• R
• Mosek
• NNLS
• EBayesThresh
• Julia
• JuMP
• REBayes
• LowRankApprox.jl
• GWAS Catalog

Cites Work

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• Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
• Deconvolution of a Distribution Function
• Nonmonotone Spectral Projected Gradient Methods on Convex Sets
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
• Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules
• Tackling Box-Constrained Optimization via a New Projected Quasi-Newton Approach
• JuMP: A Modeling Language for Mathematical Optimization
• A Stochastic Approximation Method
• GWAS Catalog

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