On the consistency of least squares estimator in models sampled at random times driven by long memory noise: the Jittered case.
dc.contributor.author | Araya, Héctor | |
dc.contributor.author | Bahamonde, Natalia | |
dc.contributor.author | Fermín, Lisandro Javier | |
dc.contributor.author | Roa, Tania | |
dc.contributor.author | Torres, Soledad | |
dc.date.accessioned | 2022-11-30T02:46:10Z | |
dc.date.available | 2022-11-30T02:46:10Z | |
dc.date.issued | 2023 | |
dc.description.abstract | 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. | en_ES |
dc.facultad | Facultad de Ingeniería | en_ES |
dc.file.name | Araya_On2023.pdf | |
dc.identifier.doi | 10.5705/ss.202020.0323 | |
dc.identifier.uri | http://repositoriobibliotecas.uv.cl/handle/uvscl/7222 | |
dc.language | en | |
dc.publisher | Institute Of Statistical Science, Academia Sinica | |
dc.source | Statistica Sinica | |
dc.subject | LONG-MEMORY NOISE | en_ES |
dc.subject | LEAST SQUARES ESTIMATOR | en_ES |
dc.subject | RANDOM TIMES | en_ES |
dc.subject | REGRESSION MODEL. | en_ES |
dc.title | On the consistency of least squares estimator in models sampled at random times driven by long memory noise: the Jittered case. | |
dc.type | Articulo | |
uv.departamento | CIMFAV |
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