Parameter estimation for a discrete time model driven by fractional Poisson process

Héctor Araya; Natalia Bahamonde; Tania Roa; Soledad Torres

doi:10.1080/03610926.2021.1973504

Parameter estimation for a discrete time model driven by fractional Poisson process

Héctor Araya, Natalia Bahamonde, Tania Roa, Soledad Torres

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

1 Cita (Scopus)

Resumen

In this article, we study the parametric problem of estimating the coefficient for a discrete time model driven by a fractional Poisson noise, when high-frequency observations are given. We consider weighted least squares and maximum likelihood estimators. Thus, asymptotic behavior of the estimators is proved and a simulation study is shown to illustrate our results.

Idioma original	Inglés
Páginas (desde-hasta)	3452-3477
Número de páginas	26
Publicación	Communications in Statistics - Theory and Methods
Volumen	52
N.º	10
DOI	https://doi.org/10.1080/03610926.2021.1973504
Estado	Publicada - 2023
Publicado de forma externa	Sí

Acceder al documento

10.1080/03610926.2021.1973504

Citar esto

@article{acb7378b891e4858861a76c7a93b487f,

title = "Parameter estimation for a discrete time model driven by fractional Poisson process",

abstract = "In this article, we study the parametric problem of estimating the coefficient for a discrete time model driven by a fractional Poisson noise, when high-frequency observations are given. We consider weighted least squares and maximum likelihood estimators. Thus, asymptotic behavior of the estimators is proved and a simulation study is shown to illustrate our results.",

keywords = "62F10, 62F12, 62M09, Fractional Poisson process, long memory, maximum likelihood estimator, weighted least square estimator",

author = "H{\'e}ctor Araya and Natalia Bahamonde and Tania Roa and Soledad Torres",

note = "Publisher Copyright: {\textcopyright} 2021 Taylor & Francis Group, LLC.",

year = "2023",

doi = "10.1080/03610926.2021.1973504",

language = "English",

volume = "52",

pages = "3452--3477",

journal = "Communications in Statistics - Theory and Methods",

issn = "0361-0926",

publisher = "Taylor and Francis Ltd.",

number = "10",

}

TY - JOUR

T1 - Parameter estimation for a discrete time model driven by fractional Poisson process

AU - Araya, Héctor

AU - Bahamonde, Natalia

AU - Roa, Tania

AU - Torres, Soledad

PY - 2023

Y1 - 2023

N2 - In this article, we study the parametric problem of estimating the coefficient for a discrete time model driven by a fractional Poisson noise, when high-frequency observations are given. We consider weighted least squares and maximum likelihood estimators. Thus, asymptotic behavior of the estimators is proved and a simulation study is shown to illustrate our results.

AB - In this article, we study the parametric problem of estimating the coefficient for a discrete time model driven by a fractional Poisson noise, when high-frequency observations are given. We consider weighted least squares and maximum likelihood estimators. Thus, asymptotic behavior of the estimators is proved and a simulation study is shown to illustrate our results.

KW - 62F10

KW - 62F12

KW - 62M09

KW - Fractional Poisson process

KW - long memory

KW - maximum likelihood estimator

KW - weighted least square estimator

UR - http://www.scopus.com/inward/record.url?scp=85114645609&partnerID=8YFLogxK

U2 - 10.1080/03610926.2021.1973504

DO - 10.1080/03610926.2021.1973504

M3 - Article

AN - SCOPUS:85114645609

SN - 0361-0926

VL - 52

SP - 3452

EP - 3477

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 10

ER -

Parameter estimation for a discrete time model driven by fractional Poisson process

Resumen

Acceder al documento

Huella

Citar esto