Monitoring the parameter vector of regression models with time-to-event response in phase II processes. Issue 14 (22nd September 2017)

• Record Type: Journal Article
• Authors: Panza, C. A.; Vargas, J. A.
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 14(2017)
• Page Start: 2779
• Page End: 2798
• Publication Date: 2017-09-22
• Subjects: Deviance -- extreme value distribution -- profile monitoring -- regression model -- relative likelihood-ratio -- time-to-event -- Weibull distribution; 62P30; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1344240
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In most of the existing specialized literature, monitoring regression models are a special case of profile monitoring. However, not every regression model always represents appropriately a profile data structure. This is clearly the case of the Weibull regression model (WRM) with common shape parameter γ . Even though it might be thought that existing methodologies (especially likelihood-ratio (LRT)-based methods) for monitoring generalized linear profiles can also be successfully applied to monitoring regression models with time-to-event response, it will be shown in this paper that those methodologies work fairly acceptable just for data structures with 1000 observations at least approximately. It was found out that some corrections, often referred to as Bartlett's adjustments, are needed to be implemented in order to improve the accuracy of using the asymptotic distributional properties of the LRT statistic for carrying out the monitoring of WRM with relatively small and moderate dimensions of the available datasets. Simulation studies suggest that the use of the aforementioned corrections make the resulting charts work quite acceptable when available data structures contain 30 observations at least. Detection abilities of the proposed schemes improve as dataset dimension increases.