Titre :

Asymptotic normality of simultaneous estimators of cyclic long-memory processes

Auteur(s) :

Ayache, Antoine [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Fradon, Myriam [Auteur]
Université de Lille, Sciences et Technologies
Nanayakkara, Ravindi [Auteur]
Olenko, Andriy [Auteur]

Titre de la revue :

preprint
Electronic Journal of Statistics

Éditeur :

Shaker Heights, OH : Institute of Mathematical Statistics

Date de publication :

2022

ISSN :

1935-7524

Mot(s)-clé(s) en anglais :

Central limit theorem
cyclic long-memory
filter
wavelet
estimators of parameters
asymptotic normality

Discipline(s) HAL :

Mathématiques [math]

Résumé en anglais : [en]

Spectral singularities at non-zero frequencies play an important role in investigating cyclic or seasonal time series. The publication [2] introduced the generalized filtered method-of-moments approach to simultaneously ...
Lire la suite >Spectral singularities at non-zero frequencies play an important role in investigating cyclic or seasonal time series. The publication [2] introduced the generalized filtered method-of-moments approach to simultaneously estimate singularity location and long-memory parameters. This paper continues studies of these simultaneous estimators. A wide class of Gegenbauer-type semi-parametric models is considered. Asymptotic normality of several statistics of the cyclic and long-memory parameters is proved. New adjusted estimates are proposed and investigated. The theoretical findings are illustrated by numerical results. The methodology includes wavelet transformations as a particular case.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL