On continuous distribution functions, minimax and best invariant estimators, and integrated balanced loss functions

DOI10.1002/cjs.11217zbMath1297.62021arXiv1308.1815OpenAlexW2963115616MaRDI QIDQ2925557

Alexandre Leblanc, Éric Marchand, Mohammad Jafari Jozani

Publication date: 16 October 2014

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

Full work available at URL: https://arxiv.org/abs/1308.1815

zbMATH Keywords

minimaxinadmissibilitynonparametric estimationcumulative distribution functionrisk functionbest invariant estimatorbalanced lossintegrated lossmaxima-nomination samplingmedian-nomination samplingstrict bowl-shaped loss

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Minimax procedures in statistical decision theory (62C20)

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Cites Work

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• Randomized nomination sampling for finite populations
• Minimax invariant estimation of a continuous distribution function under entropy loss
• Minimax estimation of constrained parametric functions for discrete families of distributions
• Methodology for the invariant estimation of a continuous distribution function
• Minimax invariant estimator of a continuous distribution function under a general loss function
• The admissibility of the empirical distribution function
• Best invariant estimation of a distribution function under the Kolmogorov-Smirnov loss function
• Minimaxity of the empirical distribution function in invariant estimation
• Minimaxity of the best invariant estimator of a distribution function under the Kolmogorov-Smirnov loss
• A complete class theorem for strict monotone likelihood ratio with applications
• Minimax invariant estimator of a continuous distribution function
• Confidence intervals for quantiles in finite populations with randomized nomination sampling
• Admissibility in discrete and continuous invariant nonparametric estimation problems and in their multinomial analogs
• Minimax estimation of a cumulative distribution function
• Minimax invariant estimator of continuous distribution function under LINEX loss
• On estimation with weighted balanced-type loss function
• The Theory of Decision Procedures for Distributions with Monotone Likelihood Ratio
• Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator
• Bayes estimates under asymmetric loss
• Estimating a Distribution Function Based on Nomination Sampling
• On Estimating Distribution Functions Using Nomination Samples
• Estimating the Population Mean Using Extreme Ranked Set Sampling
• Some Minimax Invariant Procedures for Estimating a Cumulative Distribution Function