Binomial mixtures: Geometric estimation of the mixing distribution

DOI10.1214/aos/1017939148zbMath0955.62033OpenAlexW1566539461MaRDI QIDQ1578284

Graham R. Wood

Publication date: 22 November 2000

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1017939148

zbMATH Keywords

geometrymaximum likelihoodleast squarescyclic polytopesmixing distributionweighted least squaresKullback-Leibler distancemoment curvenearest pointmixture of binomial distributions

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Special polytopes (linear programming, centrally symmetric, etc.) (52B12) Nonparametric inference (62G99)

Related Items (4)

Almost sure uniqueness of a global minimum without convexity ⋮ Discrete mixture representations of parametric distribution families: geometry and statistics ⋮ Asymptotic Normality in Mixtures of Power Series Distributions ⋮ The likelihood ratio test for general mixture models with or without structural parameter

Cites Work

• The geometry of mixture likelihoods, part II: The exponential family
• The geometry of mixture likelihoods: A general theory
• Exponential family mixture models (with least-squares estimators)
• Stabile konvexe Mengen
• Binomial mixtures and finite exchangeability
• A review of reliable maximum likelihood algorithms for semiparametric mixture models
• A review of semiparametric mixture models
• Estimating true-score distrubutions in psychological testing (an empirical Bayes estimation problem)
• A Survey and Comparison of Methods for Finding All Vertices of Convex Polyhedral Sets
• Semiparametric Estimation in the Rasch Model and Related Exponential Response Models, Including a Simple Latent Class Model for Item Analysis
• Statistical Methods: The Geometric Approach
• An Algorithm for Computing the Nonparametric MLE of a Mixing Distribution
• Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
• Robust Bayesian analysis of the binomial empirical Bayes problem
• Assessing Risks Through the Determination of Rare Event Probabilities
• Geometry of moment spaces
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