References
- Bai, Y.B., Yong, J.H., Liu, C.Y., Liu, X.M., & Meng, Y. (2011). Polyline approach for approximation Hausdorff distance between planar free-form curves. Computer-Aided Design, 43, 687–698.
- Box, G.E.P., & Cox, D.R. (1964). Analysis of transformation. Journal of the Royal Statistical Society B, 26, 211–243.
- Boyles, R.A. (1994). Process capability with a symmetric tolerances. Communications in Statistics-Simulation and Computation, 23, 615–643.
- Castagliola, P. (1996). Evaluation of non-normal process capability indices using Burr’s distributions. Quality Engineering, 8, 587–593.
- Castagliola, P., Rosa, A.F.P. (2006). Monitoring of batch processes with varying durations based on the Hausdorff distance. International Journal of Reliability and Safety Engineering, 13, 1–24.
- Clements, J. (1989). Process capability calculations for non-normal distributions. Quality Progress, 22, 95–97.
- Composite Panel Association. (1999). American National Standard: Particleboard ANSI A208. 1-1999. CPA, Gaithersburg, Md, USA.
- Fogliatto, F.S. (2008). Multiresponse optimization of products with functional quality characteristics. Quality and Reliability Engineering International, 24, 927–929.
- Guevara, R.D., & Vargas, J.A. (2015). Process capability analysis for nonlinear profiles using depth functions. Quality and Reliability Engineering International,, 31, 465–487.
- Jensen, W.A., & Birch, J.B. (2009). Profile monitoring via nonlinear mixed models. Journal of Quality Technology, 41, 18–34.
- Johnson, N. (1949). Systems of frequency curves generated by methods of translation. Biometrika, 36, 149–176.
- Keshteli, R.N., Kazemzadeh, R.B., Amiri, A., & Noorossana, R. (2014). Functional process capability indices for circular profile. Quality and Reliability Engineering International, 30, 633–644.
- López-Pintado, S., & Romo, J. (2009). On the concept of depth for functional data. Journal of the American Statistical Association, 104, 718–734.
- Noorossana, R., Saghaei, A., & Amiri, A. (2011). Statistical analysis of profile monitoring. New Jersey: John Wiley & Sons.
- Pearn, W.L., & Kotz, S. (1994). Application of Clements’ method for calculating second-and third generation process capability indices for non-normal Pearsonian populations. Quality Engineering, 7, 139–145.
- R Development Core Team. (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
- Rucklidge, W. (1996). Efficient visual recognition using the Hausdorff distance. Germany: Springer.
- Somerville, S., & Montgomery, D. (1996). Process capability indices and non-normal distributions. Quality Engineering, 19, 305–316.
- Sun, Y., & Genton, M.G. (2011). Functional box plots. Journal of Computational and Graphical Statistics, 20, 316–334.
- Wang, F.K., & Guo, Y.C. (2014). Measuring process yield for nonlinear profiles. Quality and Reliability Engineering International, 30, 1333–1339.
- Williams, J.D., Woodall, W.H., & Birch, J.B. (2007). Statistical monitoring of nonlinear product and process quality profiles. Quality and Reliability Engineering International, 23, 925–941.
- Woodall, W.H. (2007). Current research in profile monitoring. Producao, 17, 420–425.
- Woodall, W.H., Spitzner, D.J., Montgomery, D.C., & Gupta, S. (2004). Using control charts to monitor process and product quality profiles. Journal of Quality Technology, 36, 309–320.