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Confidence Interval Estimation for the Variance Parameter of Stationary Processes

Author

Listed:
  • Bor-Chung Chen

    (Department of Industrial Engineering and Operations Research, Syracuse University, Syracuse, New York 13244)

  • Robert G. Sargent

    (Department of Industrial Engineering and Operations Research, Syracuse University, Syracuse, New York 13244)

Abstract

Asymptotic confidence interval estimators of the variance parameter \sigma 2 = lim n -> \infty n Var((1/n) \sum n i = 1 X i ) are described in this paper for observations X 1 , X 2 ,...,X n from a strictly stationary phi-mixing stochastic process. They are based on asymptotic properties of the standardized time series of observations from the process. The new point and interval estimators for the variance parameter are compared to the classical batch means estimator. The results show that the new estimators have asymptotic properties that clearly dominate the classical estimator. Also, asymptotic confidence interval estimators for the ratio of two variance parameters representing two independent processes are discussed.

Suggested Citation

  • Bor-Chung Chen & Robert G. Sargent, 1990. "Confidence Interval Estimation for the Variance Parameter of Stationary Processes," Management Science, INFORMS, vol. 36(2), pages 200-211, February.
    • Handle: RePEc:inm:ormnsc:v:36:y:1990:i:2:p:200-211
    DOI: 10.1287/mnsc.36.2.200
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