On the consistency of the least squares estimator in models sampled at random times driven by long memory noise: the renewal 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 this article, we prove the strong consistency of the least squares estimator in a random sampled linear regression model with long memory noise and an independent set of random times given by renewal process sampling. Additionally, we illustrate how to work with a random number of observations up to the time T = 1. A simulation study is provided to illustrate the behavior of the different terms involved and the performance of the estimator under different values of the Hurst parameter H.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameAraya_Ont2023.pdf
dc.identifier.doi10.5705/ss.202020.0457
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7223
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.subjectRENEWAL MODELen_ES
dc.titleOn the consistency of the least squares estimator in models sampled at random times driven by long memory noise: the renewal case.
dc.typeArticulo
uv.departamentoCIMFAV

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