• Modified Tukey's Control C...

Treffer: Modified Tukey's Control Chart

Title:
Modified Tukey's Control Chart
Authors:
TERCERO-GOMEZ, Victor, RAMIREZ-GALINDO, Jose, CORDERO-FRANCO, Alavarado, SMITH, Milton, BERUVIDES, Mario
Source:
Communications in statistics. Simulation and computation. 41(8-10):1566-1579
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Théorie des probabilités et processus stochastiques, Probability theory and stochastic processes, Lois de probabilités, Distribution theory, Statistiques, Statistics, Analyse multivariable, Multivariate analysis, Applications, Fiabilité, test de durée de vie, contrôle de la qualité, Reliability, life testing, quality control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse discriminante, Discriminant analysis, Análisis discriminante, Analyse multivariable, Multivariate analysis, Análisis multivariable, Analyse numérique, Numerical analysis, Análisis numérico, Carte contrôle, Control chart, Carta control, Distribution statistique, Statistical distribution, Distribución estadística, Erreur systématique, Bias, Error sistemático, Estimation biaisée, Biased estimation, Estimación sesgada, Estimation moyenne, Mean estimation, Estimación promedio, Fonction répartition, Distribution function, Función distribución, Fonction symétrique, Symmetric function, Función simétrica, Industrie, Industry, Industria, Ingénierie, Engineering, Ingeniería, Loi symétrique, Symmetric law, Ley simétrica, Moyenne, Average, Promedio, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Méthode stochastique, Stochastic method, Método estocástico, Observation aberrante, Outlier, Observación aberrante, Simulation numérique, Numerical simulation, Simulación numérica, Théorie approximation, Approximation theory, 60E05, 62E10, 62E17, 62H30, 62P30, 65C05, Classification automatique(statistiques), Coefficient bêta 1 Pearson, Coefficient of skewness, Skewness, Average run length, Box plot, Primary 62G35, Secondary 65C05, Tukey's control chart
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Industrial Engineering, Texas Tech University, Lubbock, Texas, United States
Facultad de Ciencias Fisico Mathematics, Universidad Autonoma de Nuevo Leon, San Nicolas de los Garza, Nuevo Leon, Mexico
ISSN:
0361-0918
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26164008
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.26164008
Database:
PASCAL Archive

Weitere Informationen

Phase 1 of control analysis requires large amount of data to fit a distribution and estimate the corresponding parameters of the process under study. However, when only individual observations are available, and no a priori knowledge exists, the presence of outliers can bias the analysis. A relatively recent and successful approach to address this situation is Tukey's Control Chart (TCC), a charting method that applies the Box Plot technique to estimate the control limits. This procedure has proven to be effective for symmetric distributions. However, when skewness is present the average run length performance diminishes significantly. This article proposes a modified version of TCC to consider skewness with minimum assumptions on the underlying distribution of observations. Using theoretical results and Monte Carlo simulation, the modified TCC is tested over several distributions proving a better representation of skewed populations, even in cases when only a limited number of observations are available.