On the Monitoring of Linear Profiles

By Keunpyo Kim, Mahmoud A. Mahmoud, William H. Woodall. 2003

538 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b141befa89def79ad2bfa3e28d91649b033c2ea1

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Citations in the corpus

Monitoring polynomial profiles in quality control applications (2009, background): “[7] coded the x-values to change the average to zero and make the regression parameters independent.”
Monitoring the Slopes of Linear Profiles (2009, methodology): “Kim et al. (2003) established a Phase II exponentially weighted moving average (EWMA) control chart method for monitoring linear profile data where the x-values are centered and show that their method provides better ARL performance for detecting shifts in the regression model parameters than Kang…”
Profile Monitoring with Binary Data and Random Predictors (2011, methodology): “As an alternative, we may consider the EWMA chart as in Kim et al. (2003), Zou et al.”
A CUSUM-based method for monitoring simple linear profiles (2009, methodology): “Since the ARL values for shifts δ are symmetric around δ = 0 [8], instead of the negative shifts used by Kang and Albin [5], positive shifts are used in our simulation studies.”
Phase II Monitoring of Nonlinear Profiles (2009, methodology): “Kim et al. (2003) proposed three independent univariate EWMA control charts to detect shifts in the parameters of a profile and showed that it is superior to the methods of Kang and Albin (2000).”
A Self-Starting Control Chart for Linear Profiles (2007, methodology): “The smoothing constants, λ, in Equations (5) and (6) are set equal to 0.2, as in the EWMA chart used by Kang and Albin (2000) and Kim et al. (2003).”
Statistical monitoring of nonlinear product and process quality profiles (2007, methodology): “For each of the 24 boards in the baseline sample we fit the nonlinear model in Equation (11), and calculated the T 2 C , T 2 D, and T 2 MVE statistics of Equations (6), (7), and (8), respectively, based on the β̂i values.”
Monitoring General Linear Profiles Using Multivariate Exponentially Weighted Moving Average Schemes (2007, methodology): “Gupta, Montgomery, and Woodall (2006) compared the performance of two phase II monitoring schemes of linear profiles, the control charting schemes proposed by Croarkin and Varner (1982) and Kim et al. (2003).”
Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression (2009, methodology): “By centering the x-values to make the least squares estimators of the Y -intercept (A0) and slope (A1) independent of each other, Kim et al. (2003) proposed a combined-chart scheme in which three EWMA charts designed, respectively, for detecting shifts in intercept, slope, and standard deviation (…”
Phase I Analysis of Linear Profiles With Calibration Applications (2004, methodology): “For this purpose, we recommend the third control chart of Kim et al. (2003), which is based on control limits in (15).”
Phase I monitoring of generalized linear model-based regression profiles (2015, methodology): “Zou et al. [16] proposed a method based on change point for monitoring simple linear profiles in Phase II and compared the performance of their method with those presented by Kang and Albin [1] and Kim et al.[14] Niaki et al. [17] proposed using the GLM and R control chart simultaneously for monitor”
CONTROL CHART FOR MONITORING NONPARAMETRIC PROFILES WITH ARBITRARY DESIGN (2009, background): “See, for instance, Kang and Albin (2000), Kim et al. (2003), Mahmoud and Woodall (2004), Zou et al. (2006) (2007b), and Mahmoud et al. (2007), among several others.”
Monitoring Correlation Within Linear Profiles Using Mixed Models (2008, background): “Stover and Brill (1998); Kang and Albin (2000); Kim, Mahmoud, and Woodall (2003); Mahmoud and Woodall (2004); Wang and Tsung (2005); Gupta, Montgomery, and Woodall (2006); and Mahmoud et al. (2007) all considered monitoring of linear profiles.”