29 lines
1.2 KiB
Plaintext
29 lines
1.2 KiB
Plaintext
Package: CVarE
|
|
Type: Package
|
|
Title: Conditional Variance Estimator for Sufficient Dimension Reduction
|
|
Version: 1.0
|
|
Date: 2021-03-05
|
|
Maintainer: Daniel Kapla <daniel@kapla.at>
|
|
Author: Daniel Kapla [aut, cph, cre],
|
|
Lukas Fertl [aut, cph],
|
|
Efstathia Bura [ctb]
|
|
Description: Implementation of the Conditional Variance Estimation (CVE)
|
|
Fertl and Bura (2021) <arXiv:2102.08782> and the Ensemble Conditional Variance
|
|
Estimation (ECVE) Fertl and Bura (2021) <arXiv:2102.13435> method.
|
|
|
|
CVE and ECVE are sufficient dimension reduction (SDR) 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.
|
|
License: GPL-3
|
|
Contact: <daniel@kapla.at>
|
|
URL: https://git.art-ist.cc/daniel/CVE
|
|
Encoding: UTF-8
|
|
NeedsCompilation: yes
|
|
Imports: stats,mda
|
|
RoxygenNote: 7.0.2
|