TY - JOUR
T1 - Parameter estimation in a stationary autoregressive process with correlated multiple observations
AU - Sethuraman, S.
AU - Basawa, I. V.
N1 - Funding Information:
I.V. Basawa’sw ork was partially supportedb y a grant from the Office of Naval ResearchW. e thank the refereef or constructivec omments.
Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 1994/4/15
Y1 - 1994/4/15
N2 - An autoregressive process is proposed to model time series data with multiple observations at each time point. The joint autocorrelation function for the model has a product form, the first factor being the autocorrelation function for a stationary AR(p) process and the second factor involving a constant intraclass correlation ρ. The least-squares and the Gaussian maximum likelihood estimators of the autoregression parameters θ=(θ1,...,θp)T and the intraclass correlation ρ are presented and their limit distributions are derived.
AB - An autoregressive process is proposed to model time series data with multiple observations at each time point. The joint autocorrelation function for the model has a product form, the first factor being the autocorrelation function for a stationary AR(p) process and the second factor involving a constant intraclass correlation ρ. The least-squares and the Gaussian maximum likelihood estimators of the autoregression parameters θ=(θ1,...,θp)T and the intraclass correlation ρ are presented and their limit distributions are derived.
KW - Intraclass correlation
KW - Panel time series
KW - asymptotic distributions
KW - least-squares estimation
KW - maximum likelihood estimation
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U2 - 10.1016/0378-3758(94)90203-8
DO - 10.1016/0378-3758(94)90203-8
M3 - Article
AN - SCOPUS:38149147857
VL - 39
SP - 137
EP - 154
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
SN - 0378-3758
IS - 2
ER -