Designing penalty functions in high dimensional problems: the role of tuning parameters

DOI10.1214/16-EJS1169zbMath1397.62259WikidataQ42130054 ScholiaQ42130054MaRDI QIDQ309586

Ting-Huei Chen, Jason P. Fine, Wei Sun

Publication date: 7 September 2016

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1472498029

zbMATH Keywords

folded-concave penaltiesgenome-wide association studiestuning parameter selection

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (4)

Designing penalty functions in high dimensional problems: the role of tuning parameters ⋮ A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease ⋮ PenPC : A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs ⋮ A Penalized Regression Framework for Building Polygenic Risk Models Based on Summary Statistics From Genome-Wide Association Studies and Incorporating External Information

Uses Software

• DAVID
• sparsenet

Cites Work

• Unnamed Item
• Unnamed Item
• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• Nearly unbiased variable selection under minimax concave penalty
• A unified approach to model selection and sparse recovery using regularized least squares
• Designing penalty functions in high dimensional problems: the role of tuning parameters
• On constrained and regularized high-dimensional regression
• One-step sparse estimates in nonconcave penalized likelihood models
• Estimating the dimension of a model
• Calibrating nonconvex penalized regression in ultra-high dimension
• Extended BIC for small-n-large-P sparse GLM
• SparseNet: Coordinate Descent With Nonconvex Penalties
• Extended Bayesian information criteria for model selection with large model spaces
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Genomic Control for Association Studies
• Nonconcave Penalized Likelihood With NP-Dimensionality

This page was built for publication: Designing penalty functions in high dimensional problems: the role of tuning parameters