ArticleBiometrics2012
High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis.
Article in Biometrics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- HIGH-DIMENSIONAL NEWEY-POWELL TEST VIA APPROXIMATE MESSAGE PASSING.Econometric theory · 2026Article
- The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.Statistics in medicine · 2024Article
- Article
- Bayesian Hidden Markov Models for Dependent Large-Scale Multiple Testing.Computational statistics & data analysis · 2019Article
- Tractable Bayesian variable selection: beyond normality.Journal of the American Statistical Association · 2018Article
- Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq.Nucleic acids research · 2017Article
- Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action.Human molecular genetics · 2016Article
- Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics.BMC genomics · 2014Article
- Additive, epistatic, and environmental effects through the lens of expression variability QTL in a twin cohort.Genetics · 2014Article
- Statistical analysis reveals co-expression patterns of many pairs of genes in yeast are jointly regulated by interacting loci.PLoS genetics · 2013Article
- Article
- Robust Gaussian graphical modeling via l1 penalization.Biometrics · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We consider the problem of high-dimensional regression under non-constant error variances. Despite being a common phenomenon in biological applications, heteroscedasticity has, so far, been largely ignored in high-dimensional analysis of genomic data sets. We propose a new methodology that allows non-constant error variances for high-dimensional estimation and model selection. Our method incorporates heteroscedasticity by simultaneously modeling both the mean and variance components via a novel doubly regularized approach. Extensive Monte Carlo simulations indicate that our proposed procedure can result in better estimation and variable selection than existing methods when heteroscedasticity arises from the presence of predictors explaining error variances and outliers. Further, we demonstrate the presence of heteroscedasticity in and apply our method to an expression quantitative trait loci (eQTLs) study of 112 yeast segregants. The new procedure can automatically account for heteroscedasticity in identifying the eQTLs that are associated with gene expression variations and lead to smaller prediction errors. These results demonstrate the importance of considering heteroscedasticity in eQTL data analysis.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.