Fixed and random effects selection in linear and logistic models.
We address the problem of selecting which variables should be included in the fixed and random components of logistic mixed effects models for correlated data. A fully Bayesian variable selection is implemented using a stochastic search Gibbs sampler to estimate the exact model-averaged posterior distribution. This approach automatically identifies subsets of predictors having nonzero fixed effect coefficients or nonzero random effects variance, while allowing uncertainty in the model selection process. Default priors are proposed for the variance components and an efficient parameter expansion Gibbs sampler is developed for posterior computation. The approach is illustrated using simulated data and an epidemiologic example.
Kinney
Satkartar K
SK
Institute of Statistics and Decision Sciences, Duke University, Box 90251, Durham, North Carolina 27705, USA. saki@stat.duke.edu
Dunson
David B
DB
D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D016015:Logistic Models; D008954:Models, Biological; D011897:Random Allocation; D016008:Statistical Distributions