Bayesian analysis of growth curves with AR(1) dependence
The generalized growth curve model with AR(1) dependence is defined as \(Y=X\tau A+\varepsilon\), where \(\tau\) is an unknown vector and \(X\) and \(A\) are known design matrices. The random error vector \(\varepsilon\) is normal with zero mean vector and covariance matrix \(\Sigma=\sigma^2C\), where the matrix \(C\) has the form \((\rho^{| i-j|})\). \(\sigma^2\), \(\sigma^2>0\), and \(\rho\), \(|\rho|<1\), are unknown parameters. The authors consider Bayesian analysis of this model. Both parameter estimation and prediction of future values are discussed. Results are illustrated with real and simulated data.
scientific article; zbMATH DE number 1115637
scientific article