Local asymptotic normality for regression models with long-memory disturbance
Hallin, Marc ; Taniguchi, Masanobu ; Serroukh, Abdeslam ; Choy, Kokyo
Ann. Statist., Tome 27 (1999) no. 4, p. 2054-2080 / Harvested from Project Euclid
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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.
Publié le : 1999-12-14
Classification:  Long-memory process,  FARIMA model,  local asymptotic normality,  locally asymptotically optimal test,  adaptive estimation,  discriminant analysis,  60G10,  62E20,  62A10,  62F05 
@article{1017939250,
     author = {Hallin, Marc and Taniguchi, Masanobu and Serroukh, Abdeslam and Choy, Kokyo},
     title = {Local asymptotic normality for regression models with long-memory
		 disturbance},
     journal = {Ann. Statist.},
     volume = {27},
     number = {4},
     year = {1999},
     pages = { 2054-2080},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1017939250}
}

Hallin, Marc; Taniguchi, Masanobu; Serroukh, Abdeslam; Choy, Kokyo. Local asymptotic normality for regression models with long-memory
		 disturbance. Ann. Statist., Tome 27 (1999) no. 4, pp.  2054-2080. http://gdmltest.u-ga.fr/item/1017939250/