A semiparametric nonlinear mixed model approach to phase I profile monitoring. Issue 6 (3rd July 2019)

• Record Type: Journal Article
• Authors: Gomaa, Abdel-Salam; Birch, Jeffrey B.
• Is Part Of: Communications in statistics. Volume 48:Issue 6(2019)
• Page Start: 1677
• Page End: 1693
• Publication Date: 2019-07-03
• Subjects: Model misspecification -- Nonlinear mixed model robust regression -- P-spline -- Bioassay -- Quality control -- Robust profile monitoring; Quality Control; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1422751
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: When process data follow a particular curve in quality control, profile monitoring is suitable and appropriate for assessing process stability. Previous research in profile monitoring focusing on nonlinear parametric ( P ) modeling, involving both fixed and random-effects, was made under the assumption of an accurate nonlinear model specification. Lately, nonparametric ( NP ) methods have been used in the profile monitoring context in the absence of an obvious linear P model. This study introduces a novel technique in profile monitoring for any nonlinear and auto-correlated data. Referred to as the nonlinear mixed robust profile monitoring ( NMRPM ) method, it proposes a semiparametric ( SP ) approach that combines nonlinear P and NP profile fits for scenarios in which a nonlinear P model is adequate over part of the data but inadequate of the rest. These three methods ( P, NP, and NMRPM ) account for the auto-correlation within profiles and treats the collection of profiles as a random sample with a common population. During Phase I analysis, a version of Hotelling's T 2 statistic is proposed for each approach to identify abnormal profiles based on the estimated random effects and obtain the corresponding control limits. The performance of the NMRPM method is then evaluated using a real data set. Results reveal that the NMRPM method is robust to model misspecification and performs adequately against a correctly specified nonlinear P model. Control charts with the NMRPM method have excellent capability of detecting changes in Phase I data with control limits that are easily computable.