null

<p>Null space basis of given matrix `V`</p>

internal

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.

Daniel Kapla

CVarE

Conditional Variance Estimator for Sufficient Dimension
Reduction

Lukas Fertl

Efstathia Bura

null function

Null space basis of given matrix `V` — null

Null space basis of given matrix `V`

null: Null space basis of given matrix `V`

Description

Usage

Arguments

Value