Keywords: Principal component analysis, Independent component analysis, Non-Gaussian component analysis, Sliced inverse regression
Webpages:
https://CRAN.R-project.org/package=ICtest Choosing the number of components to retain is a crucial step in every dimension reduction method. The package
ICtest introduces various tools for estimating the number of interesting components, or the true reduced dimension, in three classical situations: principal component analysis, independent component analysis and reducing the number of covariates in prediction. The estimation methods are provided in the form of hypothesis tests and in each of the three cases tests based both on asymptotic distributions and on bootstrapping are provided. The talk goes to shortly introduce the used methodology and showcase the package in action.
References Nordhausen, Klaus, Hannu Oja, and David E. Tyler. 2016. “Asymptotic and Bootstrap Tests for Subspace Dimension.”
arXiv:1611.04908.
Nordhausen, Klaus, Hannu Oja, David E. Tyler, and Joni Virta. 2017. “Asymptotic and Bootstrap Tests for the Dimension of the Non-Gaussian Subspace.”
arXiv:1701.06836.
Virta, Joni, Klaus Nordhausen, and Hannu Oja. 2016. “Projection Pursuit for Non-Gaussian Independent Components.”
arXiv:1612.05445.
