Inferring the incidence of industry inefficiency from DEA estimates

• Published in: European journal of operational research Vol. 224; no. 2; pp. 414 - 424
• Main Authors: Friesner, Daniel, Mittelhammer, Ron, Rosenman, Robert
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.01.2013; Elsevier; Elsevier Sequoia S.A
• Subjects: Applied probability; Applied sciences; Bayesian analysis; Bayesian inference; Data envelopment analysis; Decision making units; Decision theory. Utility theory; Exact sciences and technology; Linear programming; Mathematical programming; Mathematics; Nonparametric inference; Operational research and scientific management; Operational research. Management science; Physicians; Probability and statistics; Sciences and techniques of general use; Statistics; Studies; Applied probability; Bayesian inference; Data envelopment analysis; Bayes estimation; Performance evaluation; Productivity; Statistical analysis; Probabilistic approach; Prior distribution; Decision making; Empirical method; Non parametric method; Non parametric estimation; Inference; Linear programming; Latent variable model; Replacement; Modeling; Latent variable; Sampling; Categorization; Posterior distribution

► We use Bayesian methodology to infer the incidence of inefficiency in an industry. ► We place minimal prior information on the nature of the DEA efficiency frontier. ► Our methodology applies to finite populations and sampling without replacement. ► We account for the fact that DEA estimates may m...