Abstract #300477

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JSM 2002 Abstract #300477
Activity Number: 403
Type: Contributed
Date/Time: Thursday, August 15, 2002 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences*
Abstract - #300477
Title: Bayesian Analysis of a General Growth Curve Model With Box-Cox Transformation, Random Effects and ARMA(p, q) Dependence
Author(s): Jack Lee*+ and Tsung-I Lin
Affiliation(s): National Chiao Tung University and National Chiao Tung University
Address: 1001 TA Hsueh Road, Hsinchu, International, 30050, Taiwan
Keywords: Bayesian Prediction ; Gibbs Sampling ; MCMC ; Posterior distributions ; Priors ; Reparameterization
Abstract:

In this paper, from a Bayesian point of view, we consider estimation of parameters and prediction of future values for the general growth model with Box-Cox transformation, random effects and ARMA (p,q) dependence. Two prior distributions are proposed and put into comparisons in parameter estimation and prediction of future values. Reparameterization schemes (Monahan, 1984, Biometrika 71, 403-404) from the ARMA (p,q) parameters are proposed to allow for a uniform prior and benefit the parameter estimation. Markov chain Monte Carlo methods are also used to obtain more accurate Bayesian inference for parameters as well as prediction of future values. Numerical results are illustrated using fatigue-crack growth data from Bogdanoff and Kozin (1985, Probability Model of Cumulative Damage, Wiely) and simulated data.

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