Adaptive subspace methods for high-dimensional variable selection

to connected objects

Alternative title: Adaptive Subspace Methoden für hoch-dimensionale Variablenselektion

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: RWTH Aachen University, Dissertation, 2018

Event: Veröffentlichung

(where): Aachen

(who): Universitätsbibliothek der RWTH Aachen

(when): 2018

Creator: Staerk, Christian

Contributor: Kateri, Maria
Ntzoufras, Ioannis
Cramer, Erhard

DOI: 10.18154/RWTH-2018-226562

URN: urn:nbn:de:101:1-2019031205464938505728

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:48 PM CET

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Staerk, Christian
Kateri, Maria
Ntzoufras, Ioannis
Cramer, Erhard
Universitätsbibliothek der RWTH Aachen

Time of origin

2018

Other Objects (12)

High-dimensional variable selection via low-dimensional adaptive learning

Hochschulschrift

Automatic subspace clustering for high-dimensional data

Analyzing High-Dimensional Data by Subspace Validity

Hochschulschrift

Spatially adaptive sparse grids for high-dimensional problems

Adaptive concepts for high-dimensional stochastic differential equations

Arbeitspapier

Variable selection and estimation in high-dimensional models

Entanglement protection of high-dimensional states by adaptive optics

Feature Selection for High-Dimensional Data with RapidMiner

High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression

Arbeitspapier

Inference on treatment effects after selection amongst high-dimensional controls

Arbeitspapier

Inference on treatment effects after selection amongst high-dimensional controls

High-dimensional variable selection via low-dimensional adaptive learning

Hochschulschrift

Automatic subspace clustering for high-dimensional data

Analyzing High-Dimensional Data by Subspace Validity

Hochschulschrift

Spatially adaptive sparse grids for high-dimensional problems

Adaptive concepts for high-dimensional stochastic differential equations

Arbeitspapier

Variable selection and estimation in high-dimensional models

Entanglement protection of high-dimensional states by adaptive optics

Feature Selection for High-Dimensional Data with RapidMiner

High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression

Arbeitspapier

Inference on treatment effects after selection amongst high-dimensional controls

Arbeitspapier

Inference on treatment effects after selection amongst high-dimensional controls

High-dimensional variable selection via low-dimensional adaptive learning

Hochschulschrift

Automatic subspace clustering for high-dimensional data

Analyzing High-Dimensional Data by Subspace Validity

Hochschulschrift

Spatially adaptive sparse grids for high-dimensional problems

Adaptive concepts for high-dimensional stochastic differential equations

Arbeitspapier

Variable selection and estimation in high-dimensional models

Entanglement protection of high-dimensional states by adaptive optics

Feature Selection for High-Dimensional Data with RapidMiner

High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression

Arbeitspapier

Inference on treatment effects after selection amongst high-dimensional controls

Arbeitspapier

Inference on treatment effects after selection amongst high-dimensional controls

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Adaptive subspace methods for high-dimensional variable selection

Object Details

Contributors, Places and Time

Further information

Data provider

Associated

Time of origin

Other Objects (12)

High-dimensional variable selection via low-dimensional adaptive learning

Automatic subspace clustering for high-dimensional data

Analyzing High-Dimensional Data by Subspace Validity

Spatially adaptive sparse grids for high-dimensional problems

Adaptive concepts for high-dimensional stochastic differential equations

Variable selection and estimation in high-dimensional models

Entanglement protection of high-dimensional states by adaptive optics

Feature Selection for High-Dimensional Data with RapidMiner

Feature Selection for High-Dimensional Data with RapidMiner

High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression

Inference on treatment effects after selection amongst high-dimensional controls

Inference on treatment effects after selection amongst high-dimensional controls

High-dimensional variable selection via low-dimensional adaptive learning

Automatic subspace clustering for high-dimensional data

Analyzing High-Dimensional Data by Subspace Validity

Spatially adaptive sparse grids for high-dimensional problems

Adaptive concepts for high-dimensional stochastic differential equations

Variable selection and estimation in high-dimensional models

Entanglement protection of high-dimensional states by adaptive optics

Feature Selection for High-Dimensional Data with RapidMiner

Feature Selection for High-Dimensional Data with RapidMiner

High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression

Inference on treatment effects after selection amongst high-dimensional controls

Inference on treatment effects after selection amongst high-dimensional controls

High-dimensional variable selection via low-dimensional adaptive learning

Automatic subspace clustering for high-dimensional data

Analyzing High-Dimensional Data by Subspace Validity

Spatially adaptive sparse grids for high-dimensional problems

Adaptive concepts for high-dimensional stochastic differential equations

Variable selection and estimation in high-dimensional models

Entanglement protection of high-dimensional states by adaptive optics

Feature Selection for High-Dimensional Data with RapidMiner

Feature Selection for High-Dimensional Data with RapidMiner

High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression

Inference on treatment effects after selection amongst high-dimensional controls

Inference on treatment effects after selection amongst high-dimensional controls

Related objects

Reset password