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Tytuł:
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Process monitoring using inflated beta regression control chart.
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Autorzy:
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Lima-Filho LMA; Departamento de Estatística, Universidade Federal da Paraíba, João Pessoa, Brazil.
Pereira TL; Departamento de Estatística, Universidade Federal da Paraíba, João Pessoa, Brazil.
Souza TC; Departamento de Estatística, Universidade Federal da Paraíba, João Pessoa, Brazil.
Bayer FM; Departamento de Estatística and LACESM, Universidade Federal de Santa Maria, Santa Maria, Brazil.
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Źródło:
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PloS one [PLoS One] 2020 Jul 30; Vol. 15 (7), pp. e0236756. Date of Electronic Publication: 2020 Jul 30 (Print Publication: 2020).
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: San Francisco, CA : Public Library of Science
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MeSH Terms:
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Data Interpretation, Statistical*
Models, Statistical*
Monte Carlo Method*
Humans ; Regression Analysis
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References:
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J Safety Res. 2018 Jun;65:153-159. (PMID: 29776524)
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Entry Date(s):
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Date Created: 20200731 Date Completed: 20200924 Latest Revision: 20200924
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Update Code:
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20240104
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PubMed Central ID:
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PMC7392223
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DOI:
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10.1371/journal.pone.0236756
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PMID:
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32730316
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This paper provides a general framework for controlling quality characteristics related to control variables and limited to the intervals (0, 1], [0, 1), or [0, 1]. The proposed control chart is based on the inflated beta regression model considering a reparametrization of the inflated beta distribution indexed by the response mean, which is useful for modeling fractions and proportions. The contribution of the paper is twofold. First, we extend the inflated beta regression model by allowing a regression structure for the precision parameter. We also present closed-form expressions for the score vector and Fisher's information matrix. Second, based on the proposed regression model, we introduce a new model-based control chart. The control limits are obtained considering the estimates of the inflated beta regression model parameters. We conduct a Monte Carlo simulation study to evaluate the performance of the proposed regression model estimators, and the performance of the proposed control chart is evaluated in terms of run length distribution. Finally, we present and discuss an empirical application to show the applicability of the proposed regression control chart.
Competing Interests: The authors have declared that no competing interests exist.
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