Local asymptotic normality for regression models with long-memory disturbance

DOI10.1214/aos/1017939250zbMath0957.62077OpenAlexW1579201081WikidataQ115374768 ScholiaQ115374768MaRDI QIDQ1583901

Abdeslam Serroukh, Masanobu Taniguchi, Kokyo Choy, Marc Hallin

Publication date: 20 March 2001

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1017939250

zbMATH Keywords

discriminant analysislocal asymptotic normalityadaptive estimationlong-memory processFARIMA modellocally asymptotically optimal test

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Asymptotic properties of parametric tests (62F05)

Related Items (16)

Local asymptotic normality for multivariate nonlinear AR processes ⋮ Local asymptotic normality for long-memory process with strong mixing noises ⋮ Local asymptotic normality for a periodically time varying long memory parameter ⋮ Likelihood Ratio Processes under Nonstandard Settings ⋮ LOCAL ASYMPTOTIC NORMALITY OF GENERAL CONDITIONALLY HETEROSKEDASTIC AND SCORE-DRIVEN TIME-SERIES MODELS ⋮ Preliminary Multiple-Test Estimation, With Applications to k-Sample Covariance Estimation ⋮ Diagnostic checking in FARIMA models with uncorrelated but non-independent error terms ⋮ Preliminary test estimation for spectra ⋮ Modified LASSO estimators for time series regression models with dependent disturbances ⋮ Scalable Monte Carlo inference and rescaled local asymptotic normality ⋮ Estimating FARIMA models with uncorrelated but non-independent error terms ⋮ Classification and similarity analysis of fundamental frequency patterns in infant spoken language acquisition ⋮ Bent-cable regression with autoregressive noise ⋮ Preliminary Test Estimation for Regression Models with Long-Memory Disturbance ⋮ LAN theorem for non-Gaussian locally stationary processes and its applications ⋮ Efficiency improvements in inference on stationary and nonstationary fractional time series

Uses Software

• longmemo

Cites Work

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