On a General Selection Procedure for Multivariate Normal Populations: Asymptotic Approximation to Constants

DOI10.1080/01966324.1993.10737350zbMath0788.62021OpenAlexW2319530927MaRDI QIDQ4280089

Hiroto Hyakutake

Publication date: 2 June 1994

Published in: American Journal of Mathematical and Management Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01966324.1993.10737350

zbMATH Keywords

asymptotic expansionasymptotic approximationselectionindifference zonemultivariate normal populationsmean vector\(F\)-distributionpercentage pointstwo-stage sampling scheme

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Statistical tables (62Q05) Statistical ranking and selection procedures (62F07)

Related Items (3)

In Heteroscedastic Simultaneous Inference, the Heteroscedastic Method II (Based on Healy's Two-Stage Sampling) is more Economical than the Original HM (Which is Based on Chatterjee's Sampling) ⋮ The Heteroscedastic Method: Fifty+ Years of Progress 1945–2000, and Professor Minoru Siotani's Award-Winning Contributions ⋮ Fixed-Size Confidence Regions for the Multinormal Mean in an Intraclass Correlation Model

Cites Work

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• Note on the utilization of the generalized student ratio in the analysis of variance or dispersion
• A general treatment for selecting the best of several multivariate normal populations
• A multivariate solution of the multivariate ranking and selection problem
• Generalized Asymptotic Expansions of Cornish-Fisher Type

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