Linearly interpolated FDH efficiency score for nonconvex frontiers

71 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

How to improve the performances of DEA/FDH estimators in the presence of noise? (2007, unknown): “We will first define the DGP, generating points inside *F, as in Kneip et al. (1998), and then we will introduce the noise.”
Probabilistic characterization of directional distances and their robust versions (2012, methodology): “The consistency of the bootstrap using FDH estimators has been established in Jeong and Simar (2006), it is based on a subsampling approach.”
Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives (2013, methodology): “Here again the bootstrap might provide a useful alternative, but some smoothing of the FDH estimator might even be more appropriate than it is in the case of the bootstrap with DEA estimators (Jeong and Simar, 2006).”
Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non‐parametric, two‐stage models of production (2018, methodology): “Subsampling can also be used for inference in the FDH case; see Jeong and Simar (2006) and Simar and Wilson (2011).”