High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis

DOI10.1111/j.1541-0420.2011.01652.xzbMath1241.62152OpenAlexW2099413686WikidataQ34252553 ScholiaQ34252553MaRDI QIDQ2894024

Z. John Daye, Hongzhe Li, Jinbo Chen

Publication date: 27 June 2012

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3218221

zbMATH Keywords

model selectiongeneralized least squaresvariance estimationsparse regressionlarge \(p\) small \(n\)

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Monte Carlo methods (65C05) Estimation in survival analysis and censored data (62N02)

Related Items (12)

A general family of trimmed estimators for robust high-dimensional data analysis ⋮ Joint estimation and variable selection for mean and dispersion in proper dispersion models ⋮ Bayesian hidden Markov models for dependent large-scale multiple testing ⋮ Cross-Fitted Residual Regression for High-Dimensional Heteroscedasticity Pursuit ⋮ A semiparametric latent factor model for large scale temporal data with heteroscedasticity ⋮ Robust Gaussian Graphical Modeling Via l₁ Penalization ⋮ Perspective maximum likelihood-type estimation via proximal decomposition ⋮ Shrinkage and LASSO strategies in high-dimensional heteroscedastic models ⋮ Variable selection for high-dimensional regression models with time series and heteroscedastic errors ⋮ Heteroscedastic BART via Multiplicative Regression Trees ⋮ High-dimensional robust approximated \(M\)-estimators for mean regression with asymmetric data ⋮ Tractable Bayesian Variable Selection: Beyond Normality

Uses Software

• glmnet

Cites Work

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