Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules

Estimation of mixture densities for the classical Gaussian compound decision problem and their associated (empirical) Bayes rules is considered from two new perspectives. The first, motivated by Brown and Greenshtein, introduces a nonparametric maximum likelihood estimator of the mixture density sub...

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Vydané v:	Journal of the American Statistical Association Ročník 109; číslo 506; s. 674 - 685
Hlavní autori:	Koenker, Roger, Mizera, Ivan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 01.06.2014 Taylor & Francis Group, LLC Taylor & Francis Ltd
Predmet:	Algorithms Baseball Bayes estimators Bayes rule Bayesian analysis Bernoulli Hypothesis Constraints Convex analysis Density estimation Empirical Bayes Estimating techniques Estimation methods Estimators Interior points Iterative solutions Kiefer-Wolfowitz maximum likelihood estimator Maximum likelihood estimation Mixture models Mixtures Normal distribution Objective functions Optimization prediction Rules Scientific method Statistics system optimization Theory and Methods
ISSN:	1537-274X, 0162-1459, 1537-274X
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