npbr: A Package for Nonparametric Boundary Regression in R

Daouia, Abdelaati, Laurent, Thibault and Noh, Hohsuk (2017) npbr: A Package for Nonparametric Boundary Regression in R. Journal of Statistical Software, 79 (9). pp. 1-43.

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Official URL: http://tse-fr.eu/pub/30485

Abstract

The package npbr is the first free specialized software for data edge and frontier analysis in the statistical literature. It provides 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 single and multiple shape constraints. They also cover data envelopment techniques as well as robust approaches to outliers. The routines included in npbr are user friendly and afford a large degree of flexibility in the estimation specifications. They provide smoothing parameter selection for the modern local linear and polynomial spline methods as well as for some promising extreme value techniques. Also, they seamlessly allow for Monte Carlo comparisons among the implemented estimation procedures. This package will be very useful for statisticians and applied researchers interested in employing nonparametric boundary regression models. Its use is illustrated with a number of empirical applications and simulated examples

Item Type: Article
Language: English
Date: August 2017
Refereed: Yes
Place of Publication: Los Angeles, Calif :
Uncontrolled Keywords: boundary curve, concavity, extreme-values, kernel smoothing, linear programming, local linear fitting, monotonicity, multiple shape constraints, piecewise polynomials, spline smoothing, R
JEL codes: C14 - Semiparametric and Nonparametric Methods
C61 - Optimization Techniques; Programming Models; Dynamic Analysis
C63 - Computational Techniques; Simulation Modeling
C87 - Econometric Software
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 30 May 2016 10:44
Last Modified: 26 Mar 2018 09:21
OAI ID: oai:tse-fr.eu:30485
URI: http://publications.ut-capitole.fr/id/eprint/22020

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