Bayesian analysis of Box–Cox transformed linear mixed models with ARMA() dependence

Jack C. Lee, Tsung I. Lin, Kuo J. Lee, Ying L. Hsu
2005 Journal of Statistical Planning and Inference  
In this paper, we present a Bayesian inference methodology for Box-Cox transformed linear mixed model with ARMA(p, q) errors using approximate Bayesian and Markov chain Monte Carlo methods. Two priors are proposed and put into comparisons in parameter estimation and prediction of future values. The advantages of Bayesian approach over maximum likelihood method are demonstrated by both real and simulated data.
doi:10.1016/j.jspi.2004.03.015 fatcat:iamin3fckvgbpb4pltv3ynwvny