Review of Bayesian selection methods for categorical predictors using JAGSThe formulation of variable selection has been widely developed in the Bayesian literature by linking a random binary indicator to each variable. This Bayesian inference has the advantage of stochastically exploring the set of ...
This is the case for the variable selection problem, with a moderate to large number of possible explanatory variables being considered in this paper. We review some of the strategies proposed in the literature and argue that inferences based on empirical frequencies via Markov Chain Monte Carlo ...
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O’Hara RB, Sillanpaa MJ: A review of Bayesian variable selection methods: what, how and which. Bayesian Analysis 2009, 4: 85–118. Article Google Scholar Janss LLG: iBay manual version 1.47. Janss Biostatistics, Leiden, Netherlands; 2009. Google Scholar Buitenhuis B, Rontved CM, Edwards...
and Brown, 2010); see also Fahrmeir et al. (2010) for a recent review. Subsequently we consider variable selection for the more general random intercept model (1). Although this also concerns α, we will focus on variable selection for the ...
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Review model summary before simulation Save simulation results for future use Adaptive MH sampling Blocking of parameters Adaptation within each block Diminishing adaptation Random-effects parameters Control scale and covariance of the proposal distribution ...
A Kurek,K Bolejko,M Szydlowski - 《Physical Review D》 被引量: 8发表: 2008年 Bayesian variable selection for high dimensional generalized linear models: convergence rates of the fitted densities Bayesian variable selection has gained much empirical success recently in avariety of applications when ...
(2010) for a recent review.Subsequently we consider variable selection for the random intercept model (1).Although this also concerns α, we will focus on variable selection for the randomeects which, to date, has been discussed only by a few papers. Following Kinneyand Dunson (2007), Fr...
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