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

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  • Zambom, Adriano Zanin
  • Akritas, Michael G.

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.

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  • Zambom, Adriano Zanin & Akritas, Michael G., 2017. "NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i10).
    • Handle: RePEc:jss:jstsof:v:077:i10
    DOI: http://hdl.handle.net/10.18637/jss.v077.i10
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    References listed on IDEAS

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    1. Huang, Wei-Hsueh & Huang, Li-Shan & Yang, Cheng-Tao, 2022. "Invariant tests for functional data with application to an earthquake impact study," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

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