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Quasi-maximum exponential likelihood estimator and portmanteau test of double [Formula: see text] model based on [Formula: see text]

The paper studies the estimation and the portmanteau test for double [Formula: see text] model with [Formula: see text] distribution. The double [Formula: see text] model is investigated to propose firstly the quasi-maximum exponential likelihood estimator, design a portmanteau test of double [Formu...

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Detalles Bibliográficos
Autores principales: Xuan, Haiyan, Song, Lixin, Ji, Un Cig, Sun, Yan, Dai, Tianjiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132383/
https://www.ncbi.nlm.nih.gov/pubmed/30839661
http://dx.doi.org/10.1186/s13660-018-1769-9
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author Xuan, Haiyan
Song, Lixin
Ji, Un Cig
Sun, Yan
Dai, Tianjiao
author_facet Xuan, Haiyan
Song, Lixin
Ji, Un Cig
Sun, Yan
Dai, Tianjiao
author_sort Xuan, Haiyan
collection PubMed
description The paper studies the estimation and the portmanteau test for double [Formula: see text] model with [Formula: see text] distribution. The double [Formula: see text] model is investigated to propose firstly the quasi-maximum exponential likelihood estimator, design a portmanteau test of double [Formula: see text] on the basis of autocorrelation function, and then establish some asymptotic results. Finally, an empirical study shows that the estimation and the portmanteau test obtained in this paper are very feasible and more effective.
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spelling pubmed-61323832018-09-14 Quasi-maximum exponential likelihood estimator and portmanteau test of double [Formula: see text] model based on [Formula: see text] Xuan, Haiyan Song, Lixin Ji, Un Cig Sun, Yan Dai, Tianjiao J Inequal Appl Research The paper studies the estimation and the portmanteau test for double [Formula: see text] model with [Formula: see text] distribution. The double [Formula: see text] model is investigated to propose firstly the quasi-maximum exponential likelihood estimator, design a portmanteau test of double [Formula: see text] on the basis of autocorrelation function, and then establish some asymptotic results. Finally, an empirical study shows that the estimation and the portmanteau test obtained in this paper are very feasible and more effective. Springer International Publishing 2018-09-11 2018 /pmc/articles/PMC6132383/ /pubmed/30839661 http://dx.doi.org/10.1186/s13660-018-1769-9 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Xuan, Haiyan
Song, Lixin
Ji, Un Cig
Sun, Yan
Dai, Tianjiao
Quasi-maximum exponential likelihood estimator and portmanteau test of double [Formula: see text] model based on [Formula: see text]
title Quasi-maximum exponential likelihood estimator and portmanteau test of double [Formula: see text] model based on [Formula: see text]
title_full Quasi-maximum exponential likelihood estimator and portmanteau test of double [Formula: see text] model based on [Formula: see text]
title_fullStr Quasi-maximum exponential likelihood estimator and portmanteau test of double [Formula: see text] model based on [Formula: see text]
title_full_unstemmed Quasi-maximum exponential likelihood estimator and portmanteau test of double [Formula: see text] model based on [Formula: see text]
title_short Quasi-maximum exponential likelihood estimator and portmanteau test of double [Formula: see text] model based on [Formula: see text]
title_sort quasi-maximum exponential likelihood estimator and portmanteau test of double [formula: see text] model based on [formula: see text]
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132383/
https://www.ncbi.nlm.nih.gov/pubmed/30839661
http://dx.doi.org/10.1186/s13660-018-1769-9
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