Frontier estimation and extreme values theory

By Abdelaati Daouia, Jean-Pierre Florens, Léopold Simar. 2010

56 citations recorded by the source corpus.

In this paper we investigate the problem of nonparametric monotone frontier estimation from an extreme-values theory perspective. This allows to revisit the asymptotic theory of the popular Free Disposal Hull estimator in a general setup, to derive new and asymptotically Gaussian estimators and to provide useful asymptotic confidence bands for the monotone boundary function. The finite sample behavior of the suggested estimators is explored through Monte-Carlo experiments. We also apply our approach to a real data set on the production activity of the French postal services.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/1a29143e019ac4f842dc539cdf6c94e8b805dfa3

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In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Robustness and inference in nonparametric partial frontier modeling (2011, unknown): “…under which the FDH estimator ϕ̂n(x) converges to a non-degenerate distribution is given by 1 − F (y|x) = ℓx ( {ϕ(x) − y}−1 ) {ϕ(x) − y}ρx as y ↑ ϕ(x) (4.4) (Daouia et al (2010), Theorem 2.1), where ρx > 0 is a constant and ℓx is a slowly varying function, i.e., limt↑∞ ℓx(tz)/ℓx(t) = 1 for…”
Probabilistic characterization of directional distances and their robust versions (2012, background): “Recently Daouia et al. (2009, 2010) have shown that these partial frontiers estimators can even be exploited to build new estimators of the full frontier Ψ∂ being robust to extreme data points and having asymptotic normal distributions.”