Review: Reversed low-rank ANOVA model for transforming high dimensional genetic data into low dimension

DOI10.1016/j.jkss.2018.10.002zbMath1416.62415OpenAlexW2900669613MaRDI QIDQ1740304

Yoonsuh Jung, Jianhua Hu

Publication date: 30 April 2019

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jkss.2018.10.002

zbMATH Keywords

ANOVAvariable selectionBIChigh dimension

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Research exposition (monographs, survey articles) pertaining to statistics (62-02) Analysis of variance and covariance (ANOVA) (62J10)

Uses Software

• aroma.affymetrix
• ANOSVA

Cites Work

• Nearly unbiased variable selection under minimax concave penalty
• Sparse logistic principal components analysis for binary data
• Principal component analysis of binary data by iterated singular value decomposition
• Estimating the dimension of a model
• Calibrating nonconvex penalized regression in ultra-high dimension
• On the ``degrees of freedom of the lasso
• Variable selection using MM algorithms
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA
• Regularization and Variable Selection Via the Elastic Net
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Exploration, normalization, and summaries of high density oligonucleotide array probe level data

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