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Biometrika 1982 69(2):473-476; doi:10.1093/biomet/69.2.473
© 1982 by Biometrika Trust
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MISCELLANEA

An estimator of location for dependent data

METE SIRVANCI

School of Business Administration, University of Wisconsin-Milwaukee Milwaukee, Wisconsin, U.S.A.

Asymptotic properties of an estimator of location, Jn, which is an asymptotically best linear unbiased estimator for the location parameter of the logistic distribution, are studied for dependent data. It is shown that Jn is asymptotically normally distributed for strongly-mixing dependent data. The asymptotic variance of Jn is obtained in general and computed for strictly stationary first-order Gaussian autoregressive processes. The performance of Jn for dependent data is compared with that of the sample mean and the Hodges-Lehmann estimator, which is also asymptotically best unbiased for the logistic distribution.

Key Words: Asymptotic normality • Asymptotic variance • Autoregressive process • Strongly-mixing dependent data


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