Profile control charts based on nonparametric L-1 regression methods

Ying Wei, Zhibiao Zhao, Dennis K.J. Lin

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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 nonparametric L-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.

Original languageEnglish (US)
Pages (from-to)409-427
Number of pages19
JournalAnnals of Applied Statistics
Issue number1
StatePublished - Mar 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Profile control charts based on nonparametric L-1 regression methods'. Together they form a unique fingerprint.

Cite this