title: PROFILE CONTROL CHARTS BASED ON NONPARAMETRICL-1 REGRESSION METHODS.
abstract: Classical statistical process control often relies on univariate characteristics. In many contemporary applications, however, the quality of products must be characterized by some functional relation between a response variable and its explanatory variables. Monitoring such functional profiles has been a rapidly growing field due to increasing demands. This paper develops a novel nonparametricL-1 location-scale model to screen the shapes of profiles. The model is built on three basic elements: location shifts, local shape distortions, and overall shape deviations, which are quantified by three individual metrics. The proposed approach is applied to the previously analyzed vertical density profile data, leading to some interesting insights.
authors.affiliation: Department of Biostatistics, Columbia University, 722 West 168th St., New York, New York 10032, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wei
authors.forename: Zhibiao
authors.initials: Z
authors.lastname: Zhao
authors.forename: Dennis K J
authors.initials: DK
authors.lastname: Lin
keywords: Functional data; L-1 regression; nonparametric methods; profile control charts
references.citation: Ann Appl Stat. 2012 Mar 1;6(1):409-427
references.pmid: 23539524