This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 380
Type: Invited
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Technometrics
Abstract - #306042
Title: Nonparametric Profile Monitoring by Mixed Effects Modeling
Author(s): Peihua Qiu*+
Companies: University of Minnesota
Address: 313 Ford Hall, Minneapolis, MN, 55455,
Keywords: EWMA ; Local Linear Kernel Smoothing ; Nonparametric Mixed-Effects Models ; Phase II monitoring ; Profile Monitoring ; Statistical Process Control

Quality of a process is often characterized by the functional relationship between a response and one or more predictors. Profile monitoring is for checking the stability of this relationship over time. In the literature, most existing control charts are for monitoring parametric profiles, and they assume that within-profile observations are independent of each other, which is often invalid. This paper focuses on nonparametric profile monitoring when within-profile data are correlated. A novel control chart is suggested, which incorporates local linear kernel smoothing into the exponentially weighted moving average control scheme. In this method, within-profile correlation is described by a nonparametric mixed-effects model. Our proposed control chart is fast to compute and convenient to use. Numerical examples show that it works well in various cases.

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