A computationally efficient, consistent bootstrap for inference with non-parametric DEA estimators

DOI10.1007/s10614-010-9217-zzbMath1247.91139OpenAlexW2137722263MaRDI QIDQ656957

Léopold Simar, Paul W. Wilson, Alois Kneip

Publication date: 13 January 2012

Published in: Computational Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10614-010-9217-z

zbMATH Keywords

bootstrapproductivityefficiencyinferencenonparametricdata-envelopment-analysis

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Bootstrap, jackknife and other resampling methods (62F40) Nonparametric statistical resampling methods (62G09) Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.) (90C08) Statistical methods; economic indices and measures (91B82)

Related Items

Unnamed Item, Data sharpening for improving central limit theorem approximations for data envelopment analysis-type efficiency estimators, Improving finite sample approximation by central limit theorems for estimates from data envelopment analysis, Statistical inference for DEA estimators of directional distances, Statistical Approaches for Non‐parametric Frontier Models: A Guided Tour, Explaining inefficiency in nonparametric production models: the state of the art, Weak disposability in nonparametric production analysis: a new taxonomy of reference technology sets, Technical, allocative and overall efficiency: estimation and inference, WHEN BIAS KILLS THE VARIANCE: CENTRAL LIMIT THEOREMS FOR DEA AND FDH EFFICIENCY SCORES, Subsampling bootstrap in network DEA

Uses Software

• MPI

Cites Work

• ASYMPTOTICS AND CONSISTENT BOOTSTRAPS FOR DEA ESTIMATORS IN NONPARAMETRIC FRONTIER MODELS
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• On Estimation of Monotone and Concave Frontier Functions
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• Sequential Minimax Search for a Maximum
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