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A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes

Generalized Renewal Processes are useful for approaching the rejuvenation of dynamical systems resulting from planned or unplanned interventions. We present new perspectives for the Generalized Renewal Processes in general and for the Weibull-based Generalized Renewal Processes in particular. Disreg...

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Autores principales: Ferreira, Ricardo José, Firmino, Paulo Renato Alves, Cristino, Cláudio Tadeu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4511187/
https://www.ncbi.nlm.nih.gov/pubmed/26197222
http://dx.doi.org/10.1371/journal.pone.0133772
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author Ferreira, Ricardo José
Firmino, Paulo Renato Alves
Cristino, Cláudio Tadeu
author_facet Ferreira, Ricardo José
Firmino, Paulo Renato Alves
Cristino, Cláudio Tadeu
author_sort Ferreira, Ricardo José
collection PubMed
description Generalized Renewal Processes are useful for approaching the rejuvenation of dynamical systems resulting from planned or unplanned interventions. We present new perspectives for the Generalized Renewal Processes in general and for the Weibull-based Generalized Renewal Processes in particular. Disregarding from literature, we present a mixed Generalized Renewal Processes approach involving Kijima Type I and II models, allowing one to infer the impact of distinct interventions on the performance of the system under study. The first and second theoretical moments of this model are introduced as well as its maximum likelihood estimation and random sampling approaches. In order to illustrate the usefulness of the proposed Weibull-based Generalized Renewal Processes model, some real data sets involving improving, stable, and deteriorating systems are used.
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spelling pubmed-45111872015-07-24 A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes Ferreira, Ricardo José Firmino, Paulo Renato Alves Cristino, Cláudio Tadeu PLoS One Research Article Generalized Renewal Processes are useful for approaching the rejuvenation of dynamical systems resulting from planned or unplanned interventions. We present new perspectives for the Generalized Renewal Processes in general and for the Weibull-based Generalized Renewal Processes in particular. Disregarding from literature, we present a mixed Generalized Renewal Processes approach involving Kijima Type I and II models, allowing one to infer the impact of distinct interventions on the performance of the system under study. The first and second theoretical moments of this model are introduced as well as its maximum likelihood estimation and random sampling approaches. In order to illustrate the usefulness of the proposed Weibull-based Generalized Renewal Processes model, some real data sets involving improving, stable, and deteriorating systems are used. Public Library of Science 2015-07-21 /pmc/articles/PMC4511187/ /pubmed/26197222 http://dx.doi.org/10.1371/journal.pone.0133772 Text en © 2015 Ferreira et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Ferreira, Ricardo José
Firmino, Paulo Renato Alves
Cristino, Cláudio Tadeu
A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes
title A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes
title_full A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes
title_fullStr A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes
title_full_unstemmed A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes
title_short A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes
title_sort mixed kijima model using the weibull-based generalized renewal processes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4511187/
https://www.ncbi.nlm.nih.gov/pubmed/26197222
http://dx.doi.org/10.1371/journal.pone.0133772
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