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

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Authors Adriano Z. Zambom, Michael G. Akritas
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper 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 of publication 2017
Code Programming Language R
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