A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods. Its use is illustrated via a number of empirical applications and simulated examples.
|Author||Abdelaati Daouia <Abdelaati.Daouia@tse-fr.eu>, Thibault Laurent <email@example.com>, Hohsuk Noh <firstname.lastname@example.org>|
|Date of publication||2016-10-15 18:52:08|
|Maintainer||Thibault Laurent <email@example.com>|
|License||GPL (>= 2)|
dea_est: DEA, FDH and linearized FDH estimators.
green: American electric utility companies
kern_smooth: Frontier estimation via kernel smoothing
kern_smooth_bw: Bandwidth selection for kernel smoothing frontier estimators
loc_est: Local linear frontier estimator
loc_est_bw: Bandwidth selection for the local linear frontier estimator
npbr-package: Nonparametric boundary regression
nuclear: Reliability programs of nuclear reactors
poly_est: Polynomial frontier estimators
post: French postal services
quad_spline_est: Quadratic spline frontiers
quad_spline_kn: AIC and BIC criteria for choosing the optimal number of...
records: Annual sport records
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.