Open Access
March, 1992 On Best Asymptotic Confidence Intervals for Parameters of Stochastic Processes
C. C. Heyde
Ann. Statist. 20(1): 603-607 (March, 1992). DOI: 10.1214/aos/1176348545


This paper is concerned with the size of confidence intervals for parameters of stochastic processes based on limit laws with two competing normalizations, one producing asymptotic normality and the other asymptotic mixed normality. It is shown that, in a certain sense, the interval based on asymptotic normality is preferable on average. Applications to estimation of parameters in nonergodic stochastic processes and to estimation of steady-state parameters in a simulation are given to illustrate the theory.


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C. C. Heyde. "On Best Asymptotic Confidence Intervals for Parameters of Stochastic Processes." Ann. Statist. 20 (1) 603 - 607, March, 1992.


Published: March, 1992
First available in Project Euclid: 12 April 2007

zbMATH: 0745.62027
MathSciNet: MR1150367
Digital Object Identifier: 10.1214/aos/1176348545

Primary: 62F11
Secondary: 62F25 , 62M09

Keywords: asymptotic mixed normality , asymptotic normality , Best confidence intervals , nonergodic models , normalization

Rights: Copyright © 1992 Institute of Mathematical Statistics

Vol.20 • No. 1 • March, 1992
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