Campus Units

Economics, Statistics

Document Type

Conference Proceeding

Conference

26th Annual Conference on Applied Statistics in Agriculture

Publication Version

Published Version

Publication Date

2014

Journal or Book Title

Conference on Applied Statistics in Agriculture Proceedings

First Page

71

Last Page

82

DOI

10.4148/2475-7772.1004

Conference Title

26th Annual Conference on Applied Statistics in Agriculture

Conference Date

April 27-29, 2014

City

Manhattan, Kansas

Abstract

Covariance matrix estimation arises in multivariate problems including multivariate normal sampling models and regression models where random effects are jointly modeled, e.g. random-intercept, random-slope models. A Bayesian analysis of these problems requires a prior on the covariance matrix. Here we compare an inverse Wishart, scaled inverse Wishart, hierarchical inverse Wishart, and a separation strategy as possible priors for the covariance matrix. We evaluate these priors through a simulation study and application to a real data set. Generally all priors work well with the exception of the inverse Wishart when the true variance is small relative to prior mean. In this case, the posterior for the variance is biased toward larger values and the correlation is biased toward zero. This bias persists even for large sample sizes and therefore caution should be used when using the inverse Wishart prior.

Comments

This is a proceeding from the 26th Annual Conference on Applied Statistics in Agriculture, Manhattan, Kansas, April 27-29, 2014. doi: 10.4148/2475-7772.1004.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

New Prairie Press

Language

en

File Format

application/pdf

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