On a Theorem of Bahadur and Goodman
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Publication:5509333
DOI10.1214/aoms/1177699593zbMath0136.39304OpenAlexW4244745044WikidataQ100596779 ScholiaQ100596779MaRDI QIDQ5509333
Publication date: 1966
Published in: The Annals of Mathematical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aoms/1177699593
Related Items (26)
Bayes-\(P^ *\) subset selection procedures for the best population ⋮ On selecting the best treatment in a generalized linear model ⋮ Quantile estimation for a selected normal population ⋮ On selecting the best natural exponential families with quadratic variance function ⋮ Estimating the mean of the selected uniform population ⋮ Estimation of the mean of the selected gamma population ⋮ Efficiency of selection procedures ⋮ Adaptive selection of the best population ⋮ Schur-convexity of 2nd order, certain subclass of multivariate arrangement increasing functions with applications in statistics ⋮ Quantile estimation of the selected exponential population ⋮ Designing two factor experiments for selecting interactions ⋮ Estimating moments of a selected Pareto population under asymmetric scale invariant loss function ⋮ Ranking and selection of populations on the base of sample means ⋮ Inadmissibility results for the selected hazard rates ⋮ Estimating quantiles of a selected exponential population. ⋮ A recursive nonlinear predictor ⋮ Multiple decision rules for comparing several populations with a fixed known standard ⋮ On the consistency of single-stage ranking procedures ⋮ Optimal substitution of arguments of an arrangement increasing function based on sufficient statistics for parameter-arguments ⋮ Estimating parameters of a selected Pareto population ⋮ A two-sample estimate of the largest mean ⋮ Selection and Ranking Procedures–Some Personal Reminiscences, and Thoughts about its Past, Present, and Future ⋮ Estimation of regression coefficient of a selected population ⋮ Estimation of hazard rate of a selected exponential population ⋮ A decision-theoretic approach to subset selection ⋮ Closed sequential procedures for selecting the multinomial events which have the largest probabilities
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