Testing the Statistical Significance of Linear Programming Estimators

Published Online:https://doi.org/10.1287/mnsc.1050.0444

References

  • Banker R. Hypothesis tests using data envelopment analysis. J. Productivity Anal. (1996) 7:139–159CrossrefGoogle Scholar
  • Banker R., Charnes A., Cooper W. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Sci. (1984) 30:1078–1092LinkGoogle Scholar
  • Charnes A., Cooper W., Rhodes E. Measuring efficiency of decision making units. Eur. J. Oper. Res. (1978) 2:429–444CrossrefGoogle Scholar
  • Charnes A., Cooper W., Sueyoshi T. A goal programming/constrained regression review of the Bell System breakup. Management Sci. (1988) 34:1–26LinkGoogle Scholar
  • Efron B., Tibshirani R.An Introduction to the Bootstrap (1993) (Chapman & Hall, New York) CrossrefGoogle Scholar
  • Evans D., Heckman J. Natural monopoly and the Bell System: Response to Charnes, Cooper and Sueyoshi. Management Sci. (1988) 34:27–38LinkGoogle Scholar
  • Goldfeld S., Quandt R.Nonlinear Methods in Econometrics (1972) (North-Holland Publishing Company, Amsterdam, The Netherlands) Google Scholar
  • Hauser J., Shugan S. Intensity measures of consumer preference. Oper. Res. (1980) 28:278–320LinkGoogle Scholar
  • Horsky D., Nelson P. Evaluation of salesforce size and productivity through efficient frontier benchmarking. Marketing Sci. (1996) 15:301–320LinkGoogle Scholar
  • Horsky D., Rao M. Estimation of attribute weights from preference comparisons. Management Sci. (1984) 30:801–822LinkGoogle Scholar
  • Horsky D., Nelson P., Posavac S. Stating preference for the Ethereal but choosing the concrete: How the tangibility of attributes affects attribute weighting in value elicitation and choice. J. Consumer Psych. (2004) 14:132–140CrossrefGoogle Scholar
  • Jain A., Acito F., Malhotra N., Mahajan V. A comparison of the internal validity of alternative parameter estimation methods in decompositional multiattribute preference models. J. Marketing Res. (1979) 16:313–322CrossrefGoogle Scholar
  • Kamakura W., Srivastava R. An ideal-point probabilistic choice model for heterogeneous preferences. Marketing Sci. (1986) 5:199–219LinkGoogle Scholar
  • Klahr D. A Monte-Carlo investigation of the statistical significance of Kruskal's nonmetric scaling procedure. Psychometrika (1969) 34:319–330CrossrefGoogle Scholar
  • Seiford L., Thrall R. Recent developments in DEA: The mathematical programming approach to frontier analysis. J. Econometrics (1990) 46:7–38CrossrefGoogle Scholar
  • Simar L., Wilson P. Statistical inference in nonparametric frontier models: The state of the art. J. Productivity Anal. (2000) 13:49–78CrossrefGoogle Scholar
  • Srinivasan V., Shocker A. Linear programming techniques for multidimensional analysis of preferences. Psychometrika (1973) 38:337–369CrossrefGoogle Scholar
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