Sparse pathway-based prediction models for high-throughput molecular data
type: Item
instance of: scholarly article
title: Sparse pathway-based prediction models for high-throughput molecular data
zbMATH Open document ID: 1469.62095
author: Sangin Lee
author: Youngjo Lee
author: Y. Pawitan
published in: Computational Statistics and Data Analysis
publication date: 2018-08-21T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62Jxx
zbMATH DE Number: 6921197
zbMATH Keywords: hierarchical likelihood
zbMATH Keywords: overlapping groups
zbMATH Keywords: random-effect model
zbMATH Keywords: structured variable selection
MaRDI profile type: Publication
full work available at URL: j.csda.2018.04.012
OpenAlex ID: W2801117500
cites work: Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
cites work: Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
cites work: Sure Independence Screening for Ultrahigh Dimensional Feature Space
cites work: A group bridge approach for variable selection
cites work: Variable selection using MM algorithms
cites work: Q5396702
cites work: Smoothly Clipped Absolute Deviation on High Dimensions
cites work: The use of random-effect models for high-dimensional variable selection problems
cites work: A modified local quadratic approximation algorithm for penalized optimization problems
cites work: Double Hierarchical Generalized Linear Models (With Discussion)
cites work: A new sparse variable selection via random-effect model
cites work: A random-effect model approach for group variable selection
cites work: Q4864293
cites work: Model Selection and Estimation in Regression with Grouped Variables
cites work: Nearly unbiased variable selection under minimax concave penalty
Wikidata QID: Q129868218
DOI: 10.1016/J.CSDA.2018.04.012
description: scientific article; zbMATH DE number 6921197