Global convergence of the log-concave MLE when the true distribution is geometric

DOI10.1080/10485252.2013.826801zbMath1359.62139OpenAlexW2029512490MaRDI QIDQ5419453

Fadoua Balabdaoui

Publication date: 6 June 2014

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2013.826801

zbMATH Keywords

global convergencemaximum likelihoodgeometric distributiongoodness of fitlog-concaveBorel-Cantelliprobability mass function (pmf)

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20)

Related Items (2)

Log-concavity and strong log-concavity: a review ⋮ On convex least squares estimation when the truth is linear

Uses Software

• logcondens

Cites Work

• Approximation by log-concave distributions, with applications to regression
• Maximum likelihood estimation of a log-concave density and its distribution function: basic properties and uniform consistency
• Limit distribution theory for maximum likelihood estimation of a log-concave density
• Asymptotic normality of statistics based on the convex minorants of empirical distribution functions
• Estimation of a convex function: Characterizations and asymptotic theory.
• Theoretical properties of the log-concave maximum likelihood estimator of a multidimensional density
• Goodness-of-Fit Test for Monotone Functions
• Asymptotic behavior of the grenander estimator at density flat regions
• Approximations to the Neyman Type A Distribution for Practical Problems

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