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 |
Externí odkaz: |