NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection

Adriano Zanin Zambom and Michael G. Akritas

Journal of Statistical Software, 2017, vol. 077, issue i10

Abstract: We describe the R package NonpModelCheck for hypothesis testing and variable selection in nonparametric regression. This package implements functions to perform hypothesis testing for the significance of a predictor or a group of predictors in a fully nonparametric heteroscedastic regression model using high-dimensional one-way ANOVA. Based on the p values from the test of each covariate, three different algorithms allow the user to perform variable selection using false discovery rate corrections. A function for classical local polynomial regression is implemented for the multivariate context, where the degree of the polynomial can be as large as needed and bandwidth selection strategies are built in.

Date: 2017-05-03
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v077i10/v77i10.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... odelCheck_3.0.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v077i10/v77i10.R
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v077i10/v77i10.m
https://www.jstatsoft.org/index.php/jss/article/do ... v077i10/prostate.txt

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:077:i10

DOI: 10.18637/jss.v077.i10

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().