Jearkpaporn, D.; Montgomery, D. C.; Runger, G. C.; Borror, C. M. Model-based process monitoring using robust generalized linear models. (English) Zbl 1067.62119 Int. J. Prod. Res. 43, No. 7, 1337-1354 (2005). Summary: Model-based process-monitoring procedures are extremely useful in situations where an output variable of interest is impacted by one or more inputs to the process, and where there are multistage processes with multiple inputs and outputs. To build the model relating input and output variables, the procedure uses historical data, which often contain outliers. To accommodate the presence of these outliers, a robust fitting scheme is introduced for the generalized linear model in process monitoring. Robust deviance residuals are defined and used as the basis of the monitoring procedure. An example and a simulation study for a gamma-distributed response are included. The average run length performance reveals that the procedure is effective for detecting small process shifts when outliers are present. Cited in 11 Documents MSC: 62P30 Applications of statistics in engineering and industry; control charts 62J12 Generalized linear models (logistic models) Keywords:robust generalized linear models (GLMs); \(m\)-estimator; robust deviance; gamma-distributed data; simulations Software:S-PLUS; ROBETH; R PDFBibTeX XMLCite \textit{D. Jearkpaporn} et al., Int. J. Prod. Res. 43, No. 7, 1337--1354 (2005; Zbl 1067.62119) Full Text: DOI References: [1] DOI: 10.2307/1390802 [2] Hawkins DM, J. Qual. Tech. 25 pp 170– (1993) [3] Hoaglin DC, Understanding Robust and Exploratory Data Analysis (1983) [4] DOI: 10.1214/aoms/1177703732 · Zbl 0136.39805 [5] DOI: 10.1002/qre.521 [6] Marazzi A, Algorithms, Routines, and S functions for Robust Statistics: The FORTRAN Library ROBETH with an Interface to S-PLUS (1993) · Zbl 0777.62004 [7] McCullagh P, Generalized Linear Models, 2nd edn (1989) [8] Mendel BJ, J. Qual. Tech. 1 pp 1– (1969) [9] Montgomery DC, Introduction to Statistical Quality Control, 4th edn (2001) [10] Montgomery DC, Introduction to Linear Regression Analysis, 3rd edn (2001) [11] Myers RH, Classical and Modern Regression with Applications, 2nd edn (1990) [12] Myers RH, Generalized Linear Models with Applications in Engineering and the Sciences (2001) [13] DOI: 10.2307/2344614 [14] DOI: 10.2307/2289071 · Zbl 0644.62076 [15] DOI: 10.2307/2530463 [16] DOI: 10.2307/1268815 [17] DOI: 10.1214/aos/1013699996 · Zbl 1041.62019 [18] DOI: 10.1080/00207540210163964 · Zbl 1063.90034 [19] Stefanski LA, Biometrika 73 pp 413– (1986) [20] Venables WN, Modern Applied Statistics with S-Plus, 1st edn (1994) · Zbl 0806.62002 [21] Zhang GX, World Qual. Congr. Trans. 1984 pp 175– This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.