Abstract
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.
Received January 1991; revised July 1991.
AMS 1980 subject classifications. Primary 62F11; secondary 62F25,62M09.
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Heyde, C.C. (2010). On Best Asymptotic Confidence Intervals for Parameters of Stochastic Processes. In: Maller, R., Basawa, I., Hall, P., Seneta, E. (eds) Selected Works of C.C. Heyde. Selected Works in Probability and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5823-5_51
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DOI: https://doi.org/10.1007/978-1-4419-5823-5_51
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