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September, 1986 Minimum Distance Estimation and Goodness-of-Fit Tests in First-Order Autoregression
Hira L. Koul
Ann. Statist. 14(3): 1194-1213 (September, 1986). DOI: 10.1214/aos/1176350059

Abstract

This paper gives a class of minimum $L_2$-distance estimators of the autoregression parameter in the first-order autoregression model when the errors have an unknown symmetric distribution. Within the class an asymptotically efficient estimator is exhibited. The asymptotic efficiency of this estimator relative to the least-squares estimator is the same as that of a certain signed rank estimator relative to the sample mean in the one sample location model. The paper also discusses goodness-of-fit tests for testing for symmetry and for a specified error distribution.

Citation

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Hira L. Koul. "Minimum Distance Estimation and Goodness-of-Fit Tests in First-Order Autoregression." Ann. Statist. 14 (3) 1194 - 1213, September, 1986. https://doi.org/10.1214/aos/1176350059

Information

Published: September, 1986
First available in Project Euclid: 12 April 2007

zbMATH: 0607.62101
MathSciNet: MR856815
Digital Object Identifier: 10.1214/aos/1176350059

Subjects:
Primary: 62G05
Secondary: 62G10, 62G20

Rights: Copyright © 1986 Institute of Mathematical Statistics

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Vol.14 • No. 3 • September, 1986
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