Data shared Lasso: a novel tool to discover uplift
type: Item
instance of: scholarly article
title: Data shared Lasso: a novel tool to discover uplift
zbMATH Open document ID: 1466.62082
author: Samuel M. Gross
author: Robert Tibshirani
published in: Computational Statistics and Data Analysis
publication date: 2018-08-15T00:00:00Z
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 6918284
zbMATH Keywords: clinical studies
zbMATH Keywords: high-dimensional regression
zbMATH Keywords: \(\ell_1\) penalization
zbMATH Keywords: multi-task learning
zbMATH Keywords: sentiment analysis
zbMATH Keywords: uplift
Wikidata QID: Q42698240
describes a project that uses: covTest
describes a project that uses: IMDB
describes a project that uses: LogicReg
describes a project that uses: glmnet
describes a project that uses: hierNet
MaRDI profile type: Publication
full work available at URL: j.csda.2016.02.015
OpenAlex ID: W2301046523
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cites work: Q4864293
cites work: Strong Rules for Discarding Predictors in Lasso-Type Problems
cites work: The composite absolute penalties family for grouped and hierarchical variable selection
DBLP publication ID: journals/csda/GrossT16
DOI: 10.1016/J.CSDA.2016.02.015
description: scientific article; zbMATH DE number 6918284