Matching via majorization for consistency of product quality

References

• Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Sturmer, T. (2006). Variable selection for propensity score models. American Journal of Epidemiology, 163, 1149–1156.
   PubMed Web of Science ®Google Scholar
• Cochran, W. G., & Rubin, D. B. (1973). Controlling bias in observational studies: A review. Sankhya Series A, 35, 417–446.
   Google Scholar
• Egozcue, M., & Wong, W. K. (2010). Gains from diversification on convex combinations: A majorization and stochastic dominance approach. European Journal of Operational Research, 200, 893–900.
   Web of Science ®Google Scholar
• Greenwood, E. (1945). Experimental sociology: A study in method. New York, NY: Kings Crown Press.
   Google Scholar
• Hansen, B. B., & Klopfer, S. O. (2006). Optimal full matching and related designs via network flows. Journal of Computational and Graphical Statistics, 15, 609–627.
   Web of Science ®Google Scholar
• Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis, 15, 199–236.
   Web of Science ®Google Scholar
• Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86, 4–29.
   Web of Science ®Google Scholar
• Lechner, M. (2002). Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods. Journal of the Royal Statistical Society, Series A, 165, 59–82.
   Web of Science ®Google Scholar
• Lee, M. H. (2013). Variable sampling rate multivariate exponentially weighted moving average control chart with double warning lines. Quality Technology and Quantitative Management, 10, 353–368.
   Web of Science ®Google Scholar
• Li, H., & Shaked, M. (1993). Stochastic majorization of stochastically monotone families of random variables. Advances in Applied Probability, 25, 895–913.
   Web of Science ®Google Scholar
• Liu, F. Y., & Cui, L. R. (2013). A design of attributes single sampling plans for three-class products. Quality Technology and Quantitative Management, 10, 369–287.
   Web of Science ®Google Scholar
• Liu, S. W., & Wu, C. W. (2014). Design and construction of a variables repetitive group sampling plan for unilateral specification limit. Communications in Statistics-Simulation and Computation, 43, 1866–1878.
   Web of Science ®Google Scholar
• Lu, B., Zanutto, E., Hornik, R., & Rosenbaum, P. R. (2001). Matching with doses in an observational study of a media campaign against drug abuse. Journal of the American Statistical Association, 96, 1245–1253.
   PubMed Web of Science ®Google Scholar
• Marshall, A. W., Olkin, I., & Arnold, B. C. (2009). Inequalities: Theory of majorization and its applications. New York, NY: Springer.
   Google Scholar
• Morgan, S. L., & Harding, D. J. (2006). Matching estimators of causal effects: Prospects and pitfalls in theory and practice. Sociological Methods and Research, 35, 3–60.
   Web of Science ®Google Scholar
• Rachev, S. T. (1991). Probability metrics and the stability of stochastic models. New York, NY: John Wiley & Sons.
   Google Scholar
• Rosenbaum, P. R. (2002). Observational studies. New York, NY: Springer.
   Google Scholar
• Rosenbaum, P. R. (2010). Design of observational studies. New York, NY: Springer.
   Google Scholar
• Rubin, D. B. (1973). Matching to remove bias in observational studies. Biometrics, 29, 159–184.
   Web of Science ®Google Scholar
• Rüschendorf, L., & Rachev, S.T. (1998). Mass transportation problems, Vol. I. Theory (Vol. 1). New York, NY: Springer.
   Google Scholar
• Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25, 1–21.
   PubMed Web of Science ®Google Scholar
• Xu, S. H., & Li, H. (2000). Majorization of weighted trees: A new tool to study correlated stochastic systems. Mathematics of Operations Research, 25, 298–323.
   Web of Science ®Google Scholar
• Zhao, Z. (2004). Using matching to estimate treatment effects: Data requirements, matching metrics, and Monte Carlo evidence. Review of Economics and Statistics, 86, 91–107.
   Web of Science ®Google Scholar