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Asymptotic posterior distributions for balanced nested multi-way models with a large number of main effect levels

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  • Chun-Lung Su

Abstract

We derive the explicit form for the asymptotic posterior distribution of the balanced nested multi-way variance components model with the assumption that the number of the main factor levels tends to infinity while the number of any specific effect factor levels remains fixed. Under the multi-way model, we also study two different parameterizations, called the standard and the centering, and the relationship between certain quadratic forms of random effects and the variance component parameters. The asymptotic results are illustrated by a three-way model and by a simulation study under a two-way case.

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  • Chun-Lung Su, 2017. "Asymptotic posterior distributions for balanced nested multi-way models with a large number of main effect levels," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(19), pages 9425-9440, October.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:19:p:9425-9440
    DOI: 10.1080/03610926.2016.1212076
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    Cited by:

    1. Chun-Lung Su, 2021. "Bayesian multi-way balanced nested MANOVA models with random effects and a large number of the main factor levels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 663-692, July.

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