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Statistical inference and nonparametric efficiency: A selective survey

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Abstract

The purpose of this paper is to provide a brief and selective survey of statistical inference in nonparametric, deterministic, linear programming-based frontier models. The survey starts with nonparametric regularity tests, sensitivity analysis, two-stage analysis with regression, and nonparametric statistical tests. It then turns to the more recent literature which shows that DEA-type estimators are maximum likelihood, and, more importantly the results concerning the asymptotic properties of these estimators. Also included is a discussion of recent attempts to employ resampling methods to derive empirical distributions for hypothesis testing.

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  1. Department of Economics, Southern Illinois University, 62901-4515, Carbondale, IL

    S. Grosskopf

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Grosskopf, S. Statistical inference and nonparametric efficiency: A selective survey. J Prod Anal 7, 161–176 (1996). https://doi.org/10.1007/BF00157039

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