A note on second-order admissibility of the Graybill-Deal estimator of a common mean of several normal populations.

DOI10.1016/S0378-3758(96)00202-9zbMath1090.62510OpenAlexW2094798015WikidataQ57276272 ScholiaQ57276272MaRDI QIDQ1372346

Nabendu Pal, Wooi Khai Lim

Publication date: 1997

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(96)00202-9

zbMATH Keywords

Loss functionRisk functionSecond-order admissibility

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Admissibility in statistical decision theory (62C15)

Related Items (6)

A generalized \(p\)-value approach to inference on common mean ⋮ On some aspects of estimation of a common mean of two independent normal populations ⋮ Functional asymptotic confidence intervals for a common mean of independent random variables ⋮ Finite-sample properties of the Graybill-Deal estimator ⋮ A revisit to the common mean problem: comparing the maximum likelihood estimator with the Graybill-Deal estimator ⋮ On estimating common mean of several inverse Gaussian distributions

Cites Work

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• Admissible minimax estimation of a common mean of two normal populations
• Variance and distribution of the Graybill-Deal estimator of the common mean of two normal populations
• Estimation of a common mean and recovery of interblock information
• A necessary and sufficient condition for second order admissibility with applications to Berkson's bioassay problem
• On estimating the common mean of two normal distributions
• Point and confidence estimation of a common mean and recovery of interblock information
• Estimating the common mean of several normal populations
• Estimation of the common mean of two univariate normal populations
• Estimation of location parameters from two linear models under normality
• Admissibility for Estimation with Quadratic Loss
• Bayes and Fiducial Equivariant Estimators of the Common Mean of Two Normal Distributions
• Combining Unbiased Estimators

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