ON THE CONSISTENCY OF LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE JITTERED CASE

Héctor Araya, Natalia Bahamonde, Lisandro Fermín, Tania Roa, Soledad Torres

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

3 Citas (Scopus)

Resumen

In numerous applications, data are observed at random times. Our main purpose is to study a model observed at random times that incorporates a long-memory noise process with a fractional Brownian Hurst exponent H. We propose a least squares estimator in a linear regression model with long-memory noise and a random sampling time called “jittered sampling”. Specifically, there is a fixed sampling rate 1/N, contaminated by an additive noise (the jitter) and governed by a probability density function supported in [0,1/N]. The strong consistency of the estimator is established, with a convergence rate depending on N and the Hurst exponent. A Monte Carlo analysis supports the relevance of the theory and produces additional insights, with several levels of long-range dependence (varying the Hurst index) and two different jitter densities.

Idioma originalInglés
Páginas (desde-hasta)331-351
Número de páginas21
PublicaciónStatistica Sinica
Volumen33
N.º1
DOI
EstadoPublicada - ene. 2023

Huella

Profundice en los temas de investigación de 'ON THE CONSISTENCY OF LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE JITTERED CASE'. En conjunto forman una huella única.

Citar esto