In this paper, we consider an extension of the covariance structure to an AR(g) process with random effects via Bayesian and maximum-likelihood (ML) approaches. We study both parameter estimation and prediction of future values. Numerical results are illustrated with real and simulated data.
為了持續優化網站功能與使用者體驗,本網站將Cookies分析技術用於網站營運、分析和個人化服務之目的。
若您繼續瀏覽本網站,即表示您同意本網站使用Cookies。