文章基本信息

标题：npbr: A Package for Nonparametric Boundary Regression in R
本地全文：下载
作者：Abdelaati Daouia ; Thibault Laurent ; Hohsuk Noh 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2017
卷号：79
期号：1
页码：1-43
DOI：10.18637/jss.v079.i09
语种：English
出版社：University of California, Los Angeles
摘要：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.
关键词：boundary curve;concavity;extreme-values;kernel smoothing;linear programming;local linear fitting;monotonicity;multiple shape constraints;piecewise polynomials;spline smoothing;R