One-Sided Shewhart-type Charts for Monitoring the Coefficient of Variation in Short Production Runs

References

• Calabrese, J. (1995). Bayesian process control for attributes. Management Science, 41(4), 637–645.
   Google Scholar
• Calzada, M. and Scariano, S. (2013). A synthetic control chart for the coefficient of variation. Journal of Statistical Computation and Simulation, 85(5), 853–867.
   Google Scholar
• Castagliola, P., Achouri, A., Taleb, H., Celano, G. and Psarakis, S. (2013). Monitoring the coefficient of variation using a variable sampling interval control chart. Quality and Reliability Engineering International, 29(8), 1135–1149.
   Google Scholar
• Castagliola, P., Achouri, A., Taleb, H., Celano, G. and Psarakis, S. (2013). Monitoring the coefficient of variation using control charts with run rules. Quality Technology and Quantitative Management, 10(1), 75–94.
   Google Scholar
• Castagliola, P., Celano, G., Fichera, S. and Nenes, G. (2013). The variable sample size t control chart for monitoring short production runs. International Journal of Advanced Manufacturing Technology, 66(9), 1353–1366.
   Google Scholar
• Castagliola, P., Celano, G. and Psarakis, S. (2011). Monitoring the co-efficient of variation using EWMA charts. Journal of Quality Technology, 43(3), 249–265.
   Google Scholar
• Celano, G., Castagliola, P., Fichera, S. and Trovato, E. (2011). Shewhart and EWMA t charts for short production runs. Quality Reliability Engineering International, 27(3), 313–326.
   Google Scholar
• Del Castillo, E. and Montgomery, D. (1993). Optimal design of control charts for monitoring short production runs. Economic Quality Control, 8(4), 225–240.
   Google Scholar
• Del Castillo, E. and Montgomery, D. (1996). A general model for the optimal economic design of X charts used to control short or long run processes. IIE Transactions, 28(3), 193–201.
   Google Scholar
• Hendricks, W. and Robey, W. (1936). The sampling distribution of the coefficient of variation. Annals of Mathematical Statistic, 7(3), 129–132.
   Google Scholar
• Hong, E., Kang, C., Baek, J. and Kang, H. (2008). Development of CV control chart using EWMA technique. Journal of the Society of Korea Industrial and Systems Engineering, 31(4), 114–120.
   Google Scholar
• Iglewicz, B. and Myers, R. (1970). Comparisons of approximations to the percentage points of the sample coefficient of variation. Technometrics, 12(1), 166–169.
   Google Scholar
• Iglewicz, B., Myers, R. and Howe, R. (1968). On the percentage points of the sample coefficient of variation. Biometrika, 55(3), 580–581.
   Google Scholar
• Kang, C., Lee, M., Seong, Y. and Hawkins, D. (2007). A control chart for the coefficient of variation. Journal of Quality Technology, 39(2), 151–158.
   Google Scholar
• Ladany, S. (1973). Optimal use of control charts for controlling current production. Management Science, 19(7), 763–772.
   Google Scholar
• Ladany, S. and Bedi, D. (1976). Selection of the optimal setup policy. Naval Research Logistics Quaterly, 23(2), 219–233.
   Google Scholar
• Mahmoudvand, R. and Hassani, H. (2009). Two new confidence intervals for the coefficient of variation in a normal distribution. Journal of Applied Statistics, 36(4), 429–442.
   Google Scholar
• McKay, A. (1932). Distribution of the coefficient of variation and Extended t Distribution. Journal of the Royal Statistical Society, 95(4), 695–698.
   Google Scholar
• Nenes, G. and Tagaras, G. (2005). The CUSUM chart for monitoring short production run. In Proceedings of 5 th International Conference on Analysis of Manufacturing Systems — Production Management, 43–50, Zakynthos Island, Greece.
   Google Scholar
• Nenes, G. and Tagaras, G. (2007). The economically designed two sided Bayesian X control chart. European Journal of Operational Research, 183(1), 263–277.
   Google Scholar
• Nenes, G. and Tagaras, G. (2010). Evaluation of CUSUM charts for finite-horizon processes. Communications in Statistics-Simulation and Computation, 39(3), 578–597.
   Google Scholar
• Reed, G., Lynn, F., and Meade, B. (2002). Use of coefficient of variation in assessing variability of quantitative assays. Clinical and Diagnostic Laboratory Immunology, 9(6), 1235–1239.
   Google Scholar
• Reh, W. and Scheffler, B. (1996). Significance tests and confidence intervals for coefficients of variation. Computational Statistics & Data Analysis, 22(4), 449–452.
   Google Scholar
• Sharpe, W. (1994). The sharpe ratio. Journal of Portfolio Management, 21(1), 49–58.
   Google Scholar
• Tagaras, G. (1996). Dynamic control charts for finite production runs. European Journal of Operational Research, 91(1), 38–55.
   Google Scholar
• Tagaras, G. and Nikolaidis, Y. (2002). Comparing the effectiveness of various Bayesian X control charts. Operations Research, 50(5), 878–888.
   Google Scholar
• Tian, L. (2005). Inferences on the common coefficient of variation. Statistics in Medicine, 24(14), 2213–2220.
   Google Scholar
• Vangel, M. (1996). Confidence intervals for a normal coefficient of variation. American Statistician, 15(1), 21–26.
   Google Scholar
• Verrill, S. and Johnson, R. (2007). Confidence bounds and hypothesis tests for normal distribution coefficients of variation. Communications in Statistics — Theory and Methods, 36(12), 2187–2206.
   Google Scholar
• Warren, W. (1982). On the adequacy of the chi-squared approximation for the coefficient of variation. Communications in Statistics — Simulation and Computation, 11(6), 659–666.
   Google Scholar