References
- Mahmoud MA, Woodall WH. Phase I analysis of linear profiles with calibration applications. Technometrics. 2004;46(4):380–391.
- Woodall WH, Spitzner DJ, Montgomery DC, et al. Using control charts to monitor process and product quality profiles. J Qual Technol. 2004;36(3):309–320.
- Mahmoud MA. Phase I analysis of multiple linear regression profiles. Comm Statist Simulation Comput. 2008;37(10):2106–2130.
- Zou C, Qiu P. Multivariate statistical process control using lasso. J Amer Statist Assoc. 2009;104(488):1586–1596.
- Wang K, Jiang W. High-dimensional process monitoring and fault isolation via variable selection. J Qual Technol. 2009;41:247–258.
- Paynabar K, Zou C, Qiu P. A change-point approach for phase-i analysis in multivariate profile monitoring and diagnosis. Technometrics. 2016;58(2):191–204.
- Ren H, Chen N, Wang Z. Phase-II monitoring in multichannel profile observations. J Qual Technol. 2019;51(4):338–352.
- Qiu P, Zou C, Wang Z. Nonparametric profile monitoring by mixed effects modeling. Technometrics. 2010;52(3):265–277.
- Zhang H, Albin S. Detecting outliers in complex profiles using a χ2 control chart method. IIE Trans. 2009;41(4):335–345.
- Yu G, Zou C, Wang Z. Outlier detection in functional observations with applications to profile monitoring. Technometrics. 2012;54(3):308–318.
- Barnett V, Lewis T. Outliers in statistical data. 3rd ed. New York: Wiley; 1994.
- Zou C, Tseng S-T, Wang Z. Outlier detection in general profiles using penalized regression method. IIE Trans. 2014;46(2):106–117.
- Li Z, Shang Y, He Z. Phase I outlier detection in profiles with binary data based on penalized likelihood. Qual Reliab Eng. 2019;35(1):1–13.
- Ren H, Chen N, Zou C. Projection-based outlier detection in functional data. Biometrika. 2017;104(2):411–423.
- Abdel-Salam ASG, Birch JB, Jensen WA. A semiparametric mixed model approach to phase I profile monitoring. Qual Reliab Eng Int. 2013;29(4):555–569.
- Gomaa AS, Birch JB. A semiparametric nonlinear mixed model approach to phase I profile monitoring. Comm Statist Simulation Comput. 2019;48(6):1677–1693.
- Ebrahimi S, Ranjan C, Paynabar K. Monitoring and root-cause diagnostics of high-dimensional data streams. J Qual Technol. 2020;54(1):20–43.
- Hadi A, Simonoff JS. Procedures for the identification of multiple outliers in linear-models. J Am Stat Assoc. 1993;88(424):1264–1272.
- Zhang JX. A consistent test of functional form via nonparametric estimation techniques. J Econom. 1996;75(2):263–289.
- Nadaraya EA. On estimating regression. Theory Probab Appl. 1964;9(1):157–159.
- Watson GS. Smooth regression analysis. Sankhyā. 1964;26(4):359–372.
- Volpe V, Manzoni S, Marani M, et al. Leave-one-out cross-validation. Berlin: Springer; 2011.
- Rice J. Bandwidth choice for nonparametric regression. Ann Statist. 1984;12(4):1215–1230.
- Hardle W, Marron JS. Optimal bandwidth selection in nonparametric regression function estimation. Ann Statist. 1985;13(4):1465–1481.
- Vieu P. Nonparametric regression: optimal local bandwidth choice. J R Stat Soc Ser B Methodol. 1991;53(2):453–464.
- Rousseeuw PJ, Van Driessen K. Computing lts regression for large data sets. Data Min Knowl Discov. 2006;12(1):29–45.
- Cerioli A. Multivariate outlier detection with high-breakdown estimators. J Am Stat Assoc. 2010;105(489):147–156.
- Ro K, Zou C, Wang Z, et al. Outlier detection for high-dimensional data. Biometrika. 2015;102(3):589–599.