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

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dc.contributor.authorAraya, Héctor
dc.contributor.authorBahamonde, Natalia
dc.contributor.authorFermín, Lisandro Javier
dc.contributor.authorRoa, Tania
dc.contributor.authorTorres, Soledad
dc.date.accessioned2022-11-30T02:46:10Z
dc.date.available2022-11-30T02:46:10Z
dc.date.issued2023
dc.description.abstractIn 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.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameAraya_On2023.pdf
dc.identifier.doi10.5705/ss.202020.0323
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7222
dc.languageen
dc.publisherInstitute Of Statistical Science, Academia Sinica
dc.sourceStatistica Sinica
dc.subjectLONG-MEMORY NOISEen_ES
dc.subjectLEAST SQUARES ESTIMATORen_ES
dc.subjectRANDOM TIMESen_ES
dc.subjectREGRESSION MODEL.en_ES
dc.titleOn the consistency of least squares estimator in models sampled at random times driven by long memory noise: the Jittered case.
dc.typeArticulo
uv.departamentoCIMFAV

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