On the consistency of least squares estimator in models sampled at random times driven by long memory noise: the Jittered case.

Fecha

2023

Profesor Guía

Formato del documento

Articulo

ORCID Autor

Título de la revista

ISSN de la revista

Título del volumen

Editor

Institute Of Statistical Science, Academia Sinica

ISBN

ISSN

item.page.issne

item.page.doiurl

Departamento o Escuela

CIMFAV

Determinador

Recolector

Especie

Nota general

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.

Descripción

Lugar de Publicación

Auspiciador

Palabras clave

LONG-MEMORY NOISE, LEAST SQUARES ESTIMATOR, RANDOM TIMES, REGRESSION MODEL.

Licencia

URL Licencia