Bootstrap inference for network construction with an application to a breast cancer microarray study

DOI10.1214/12-AOAS589zbMath1454.62353arXiv1111.5028OpenAlexW2028553898WikidataQ41893779 ScholiaQ41893779MaRDI QIDQ1951540

Yanyan Li

Publication date: 6 June 2013

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1111.5028

zbMATH Keywords

high-dimensional datamixture modelFDRGGMmodel aggregation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic graphical models (62H22)

Related Items (6)

Unnamed Item ⋮ Learning local directed acyclic graphs based on multivariate time series data ⋮ Joint estimation of heterogeneous exponential Markov random fields through an approximate likelihood inference ⋮ Bootstrap inference for network construction with an application to a breast cancer microarray study ⋮ Kernel Knockoffs Selection for Nonparametric Additive Models ⋮ Confidence graphs for graphical model selection

Uses Software

• glasso
• PMA
• HdBCS
• Bolasso

Cites Work

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• Longitudinal data analysis using generalized linear models
• Sparse inverse covariance estimation with the graphical lasso
• The Adaptive Lasso and Its Oracle Properties
• Random lasso
• Inference and characterization of multi-attribute networks with application to computational biology
• Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer
• Estimating time-varying networks
• Estimating the dimension of a model
• The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
• Analyzing bagging
• Sparse graphical models for exploring gene expression data
• Least angle regression. (With discussion)
• Bootstrap inference for network construction with an application to a breast cancer microarray study
• Sparse permutation invariant covariance estimation
• High-dimensional graphs and variable selection with the Lasso
• Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis
• Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks
• Model selection and estimation in the Gaussian graphical model
• The Structure and Function of Complex Networks
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Partial Correlation Estimation by Joint Sparse Regression Models
• Large-Scale Simultaneous Hypothesis Testing
• The Estimation of Prediction Error

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