Minkowski-Weyl Priors for Models With Parameter Constraints: An Analysis of the BioCycle Study.
We propose a general framework for performing full Bayesian analysis under linear inequality parameter constraints. The proposal is motivated by the BioCycle Study, a large cohort study of hormone levels of healthy women where certain well-established linear inequality constraints on the log-hormone levels should be accounted for in the statistical inferential procedure. Based on the Minkowski-Weyl decomposition of polyhedral regions, we propose a class of priors that are fully supported on the parameter space with linear inequality constraints, and we fit a Bayesian linear mixed model to the BioCycle data using such a prior. We observe positive associations between estrogen and progesterone levels and F2-isoprostanes, a marker for oxidative stress. These findings are of particular interest to reproductive epidemiologists.
Predoctoral Fellow, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD 20852.
Michelle R
MR
Danaher
Professor, Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD 21250.
Anindya
A
Roy
Investigator, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD 20852.
Zhen
Z
Chen
Research Fellow, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD 20852.
Sunni L
SL
Mumford
Chief and Senior Investigator, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD 20852.
Enrique F
EF
Schisterman
Bayesian inference; Extreme directions; Extreme points; Parameter restriction; Polyhedral region