Different postselection inference strategies for kernel selection, as described in "kernelPSI: a PostSelection Inference Framework for Nonlinear Variable Selection", Slim et al., Proceedings of Machine Learning Research, 2019, <http://proceedings.mlr.press/v97/slim19a/slim19a.pdf>. The strategies rest upon quadratic kernel association scores to measure the association between a given kernel and an outcome of interest. The inference step tests for the joint effect of the selected kernels on the outcome. A fast constrained sampling algorithm is proposed to derive empirical pvalues for the test statistics.
Package details 


Author  Lotfi Slim [aut, cre], Clément Chatelain [ctb], ChloéAgathe Azencott [ctb], JeanPhilippe Vert [ctb] 
Maintainer  Lotfi Slim <[email protected]> 
License  GPL (>= 2) 
Version  1.0.0 
URL  http://proceedings.mlr.press/v97/slim19a.html 
Package repository  View on CRAN 
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