Robust Bayesian experimental design and estimation for analysis of variance models using a class of normal mixtures

DOI10.1016/0378-3758(93)90024-ZzbMath0772.62043OpenAlexW2073129518MaRDI QIDQ2366579

Blaza Toman, Joseph L. Gastwirth

Publication date: 30 August 1993

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

Full work available at URL: https://doi.org/10.1016/0378-3758(93)90024-z

zbMATH Keywords

closed form solutionsposterior distributionsblock designsBayesian experimental designclass of priorstwo-way ANOVAone-way ANOVA modelfinite mixtures of normal distributionsaverage of the Bayes risksaverage posterior expected lossminimax-regret criterionnew optimality criteriarobust Bayes designs

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Bayesian inference (62F15) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (1)

Design issues for generalized linear models: a review

Uses Software

• AS 183
• elicit-normlin

Cites Work

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• Bayesian optimal experimental design for treatment-control comparisons in the presence of two-way heterogeneity
• Optimal Bayesian experimental design for linear models
• Statistical decision theory and Bayesian analysis. 2nd ed
• Robust Bayes and empirical Bayes analysis with \(\epsilon\)-contaminated priors
• Robust Bayesian experimental designs in normal linear models
• Bayes D-optimal and E-optimal block designs
• A note on Bayes designs for inference using a hierarchical linear model
• On Robust Procedures
• Efficiency Robust Two-Sample Rank Tests
• The Optimum Design of a Two-Factor Experiment Using Prior Information

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