在許多實際的製程裡，產品或製程的品質特性是由一反應變數和一個或多個解釋變數的函數關係來界定，這種反應變數和解釋變數之間的關係稱為輪廓，而其資料形式被稱為輪廓的數據。文獻上在針對一般線性輪廓做監控時，大部分都是假設隨機誤差服從常態分佈，但如果隨機誤差的分佈為厚尾分佈時，例如混合常態分佈，這樣最小平方估計量就不是有效的估計量。為了解決這個問題，我們提出利用截修資料的方式來得到穩健統計量，接著再由此統計量來建構監控輪廓製程的管制圖。此外，我們比較提出的管制圖與文獻上的管制圖的監控效力。最後，我們以一個實例來闡述我們所提出的管制圖是如何操作使用。

[1] Jensen, D. R., Hui, Y. V. and Ghare, P. M. (1984). “Monitoring an Input-Output Model for Production. I. The Control Charts”. Management Science, 30, pp. 1197-1206.
[2] Jensen, W. A. and Birch, J. B. (2009). “Profile Monitoring via Nonlinear Mixed Models”. Journal of Quality Technology, 41, pp. 18-34.
[3] Kang, L. and Albin, S. L. (2000). “On-Line Monitoring When the Process Yields a Linear Profile”. Journal of Quality Technology, 32, pp. 418-426.
[4] Kim, K., Mahmoud, M. A. and Woodall, W. H. (2003). “On the Monitoring of Linear Profiles”. Journal of Quality Technology, 35, pp. 317-328.
[5] Lawless, J. F., MacKay, R. J. and Robinson, J. A. (1999). “Analysis of Variation Trans- mission in Manufacturing Processes - Part I”. Journal of Quality Technology, 31, pp. 131-142.
[6] Lowry, C. A., Woodall, W. H., Champ, C. W. and Rigdon, S. E. (1992). “A Multivariate Exponentially Weighted Moving Average Control Chart.” Technometrics, 34, pp. 46-53.
[7] Mahmoud, M. A. and Woodall, W. H. (2004). “Phase I Monitoring of Linear Profiles with Calibration Application”. Technometrics 46, pp. 380-391.
[8] Mestek, O., Pavlik, J. and Suchánek, M. (1994). “Multivariate Control Charts: Control Charts for Calibration Curves”. Fresenius’ Journal of Analytical Chemistry, 350, pp. 344-351.
[9] Montgomery, D. C. (2009). Introduction to Statistical Quality Control, 6th edition, John Wiley, New York.
[10] Prabhu, S. S. and Runger, G. C. (1997). “Designing a Multivariate EWMA Control Chart”. Journal of Quality Technology, 29, pp. 8-15.
[11] Qiu, P., Zou, C. and Wang, Z. (2010). “Nonparametric Profile Monitoring by Mixed Effects Modeling (with discussion)”. Technometrics, 52, pp. 265-277.
[12] Ruppert, D. and Carroll, R. J. (1980). “Trimmed Least Squares Estimation in the Linear Model”. Journal of the American Statistic Association, 75, pp. 828–838.
[13] Shiau*, J.-J. H., Huang, H.-L., Lin, S.-H., and Tsai, M.-Y. (2009). "Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression". Communications in Statistics-Theory and Methods, 38, pp. 1664-1679.
[14] Stover, F. S. and Brill, R. V. (1998). “Statistical Quality Control Applied to Ion Chromatography Calibrations”. Journal of Chromatography A, 804, pp. 37–43.
[15] Walker, E. and Wright, S. P. (2002). “Comparing Curves Using Additive Models”. Journal of Quality Technology 34, pp. 118–129.
[16] Williams, J. D. (2004). “Contributions to profile monitoring and multivariate stat- istical process control”. PhD thesis. Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, UA.
[17] Williams, J. D., Woodall, W. H. and Ferry, N. M. (2007). “Statistical Monitoring
of Heteroscedastic Dose-Response Profiles from High-Throughput Screening”. Journal of Agricultural, Biological, and Environmental Statistics, 12, pp. 216-235.
[18] Zou, C., Tsung, F. and Wang, Z. (2007). “Monitoring General Linear Profiles Using Multivariate Exponentially Weighted Moving Average Schemes”. Technometrics, 49, pp. 395–408.
[19] Zou, C., Tsung, F. and Wang, Z. (2008) “Monitoring Profiles Based on Nonparametric Regression Method”. Technometrics, 50, pp. 512-526.
[20] Zou, C., Zhang, Y. and Wang, Z. (2006). “Control Chart Based on Change-Point Model for Monitoring Linear Profiles”. IIE Transactions, 38, pp. 1093-1103.