CVarE: Conditional Variance Estimator for Sufficient Dimension Reduction

Implementation of the CVE (Conditional Variance Estimation) method proposed by Fertl, L. and Bura, E. (2021) <arXiv:2102.08782> and the ECVE (Ensemble Conditional Variance Estimation) method introduced in Fertl, L. and Bura, E. (2021) <arXiv:2102.13435>. CVE and ECVE are sufficient dimension reduction methods in regressions with continuous response and predictors. CVE applies to general additive error regression models while ECVE generalizes to non-additive error regression models. They operate under the assumption that the predictors can be replaced by a lower dimensional projection without loss of information. It is a semiparametric forward regression model based exhaustive sufficient dimension reduction estimation method that is shown to be consistent under mild assumptions.

Package details

AuthorDaniel Kapla [aut, cph, cre], Lukas Fertl [aut, cph], Efstathia Bura [ctb]
MaintainerDaniel Kapla <>
Package repositoryView on CRAN
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CVarE documentation built on March 11, 2021, 5:06 p.m.