Application of copulas to multivariate control charts

Autor: Ghislain Verdier
Přispěvatelé: Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2013
Předmět:
Statistics and Probability
0209 industrial biotechnology
Multivariate statistics
education
Copula (linguistics)
02 engineering and technology
ComputingMethodologies_ARTIFICIALINTELLIGENCE
01 natural sciences
010104 statistics & probability
020901 industrial engineering & automation
Multivariate analysis of variance
Statistics
Econometrics
Multivariate t-distribution
0101 mathematics
ComputingMilieux_MISCELLANEOUS
health care economics and organizations
Mathematics
Applied Mathematics
Density estimation
musculoskeletal system
Statistical process control
Multivariate kernel density estimation
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]
surgical procedures
operative
TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS
Hotelling's T-squared distribution
Statistics
Probability and Uncertainty
human activities
Zdroj: Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference, Elsevier, 2013, 143, pp.2151--2159
ISSN: 0378-3758
1873-1171
Popis: The most popular multivariate control chart for monitoring the mean of a distribution is probably the Hotelling T2 rule. Unfortunately, this rule relies on the assumption that the distribution under control is Gaussian, which is rarely true in practice. The objective of this paper is to propose a new approach for the non-normal multivariate case. It consists in the construction of a tolerance region obtained from a density level set estimation. The method follows a “plug-in” approach in which the density of the observations is previously estimated. This estimation is conducted using copulas modeling, an increasingly popular tool in multivariate modeling.
Databáze: OpenAIRE
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