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December 1999 Local asymptotic normality for regression models with long-memory disturbance
Kokyo Choy, Marc Hallin, Abdeslam Serroukh, Masanobu Taniguchi
Ann. Statist. 27(6): 2054-2080 (December 1999). DOI: 10.1214/aos/1017939250

Abstract

The local asymptotic normality property is established for a regression model with fractional ARIMA($p, d, q$) errors. This result allows for solving, in an asymptotically optimal way, a variety of inference problems in the long-memory context: hypothesis testing, discriminant analysis, rank-based testing, locally asymptotically minimax andadaptive estimation, etc. The problem of testing linear constraints on the parameters, the discriminant analysis problem, and the construction of locally asymptotically minimax adaptive estimators are treated in some detail.

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Kokyo Choy. Marc Hallin. Abdeslam Serroukh. Masanobu Taniguchi. "Local asymptotic normality for regression models with long-memory disturbance." Ann. Statist. 27 (6) 2054 - 2080, December 1999. https://doi.org/10.1214/aos/1017939250

Information

Published: December 1999
First available in Project Euclid: 4 April 2002

zbMATH: 0957.62077
MathSciNet: MR1765628
Digital Object Identifier: 10.1214/aos/1017939250

Subjects:
Primary: 60G10 , 62E20
Secondary: 62A10 , 62F05

Keywords: adaptive estimation , discriminant analysis , FARIMA model , local asymptotic normality , locally asymptotically optimal test , Long-memory process

Rights: Copyright © 1999 Institute of Mathematical Statistics

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Vol.27 • No. 6 • December 1999
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