Local asymptotic normality for regression models with long-memory disturbance
type: Item
instance of: scholarly article
title: Local asymptotic normality for regression models with long-memory disturbance
zbMATH Open document ID: 0957.62077
author: Marc Hallin
author: Masanobu Taniguchi
author: Abdeslam Serroukh
author: Kokyo Choy
published in: The Annals of Statistics
publication date: 2001-03-20T00:00:00Z
review text: The authors derive asymptotic theory based on a local asymptotic normality (LAN) approach for a regression model with stationary non-Gaussian fractional ARIMA\((p,d,q)\) long-memory errors. It is shown that the log-likelihood ratios have the typical LAN behavior and thus the local experiments converge weakly to a Gaussian shift experiment. The results are applied to testing linear constraints on the parameters, discriminant analysis, construction of locally asymptotically minimax adaptive estimators, and other inference problems. A general formula for the distribution, under contiguous sequences of alternatives, for a class of statistics is established. The results are valid under a very large family of innovation densities.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62F05
Mathematics Subject Classification ID: 62E20
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 1523486
zbMATH Keywords: long-memory process
zbMATH Keywords: FARIMA model
zbMATH Keywords: local asymptotic normality
zbMATH Keywords: locally asymptotically optimal test
zbMATH Keywords: adaptive estimation
zbMATH Keywords: discriminant analysis
reviewed by: Jiří Anděl
describes a project that uses: longmemo
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1214/aos/1017939250
OpenAlex ID: W1579201081
Wikidata QID: Q115374768
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DOI: 10.1214/AOS/1017939250
description: scientific article; zbMATH DE number 1523486