Parameter estimation in a stationary autoregressive process with correlated multiple observations

Sankara N Sethuraman, I. V. Basawa

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)137-154
Number of pages18
JournalJournal of Statistical Planning and Inference
Volume39
Issue number2
DOIs
StatePublished - Apr 15 1994

Fingerprint

Intraclass Correlation
Autoregressive Process
Autocorrelation Function
Stationary Process
Autocorrelation
Parameter estimation
Parameter Estimation
Product Form
Autoregression
Limit Distribution
Time Series Data
Maximum Likelihood Estimator
Maximum likelihood
Least Squares
Time series
Model
Observation
Factors
Autoregressive process
Time series data

Keywords

  • Intraclass correlation
  • Panel time series
  • asymptotic distributions
  • least-squares estimation
  • maximum likelihood estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

Parameter estimation in a stationary autoregressive process with correlated multiple observations. / Sethuraman, Sankara N; Basawa, I. V.

In: Journal of Statistical Planning and Inference, Vol. 39, No. 2, 15.04.1994, p. 137-154.

Research output: Contribution to journalArticle

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