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Tytuł pozycji:

Process monitoring using inflated beta regression control chart.

Tytuł:
Process monitoring using inflated beta regression control chart.
Autorzy:
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.
Źródło:
PloS one [PLoS One] 2020 Jul 30; Vol. 15 (7), pp. e0236756. Date of Electronic Publication: 2020 Jul 30 (Print Publication: 2020).
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science
MeSH Terms:
Data Interpretation, Statistical*
Models, Statistical*
Monte Carlo Method*
Humans ; Regression Analysis
References:
J Safety Res. 2018 Jun;65:153-159. (PMID: 29776524)
Entry Date(s):
Date Created: 20200731 Date Completed: 20200924 Latest Revision: 20200924
Update Code:
20240104
PubMed Central ID:
PMC7392223
DOI:
10.1371/journal.pone.0236756
PMID:
32730316
Czasopismo naukowe
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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