In addition, the sparsity consideration in quantile regression enables the exploration of the entire conditional distribution of the response variable given the predictors and therefore yields a more comprehensive view of the important predictors. We propose a generalized OMP algorithm for variable ...
We also propose a data-dependent choice of thetuning parameter to make the method more practical, by extending the originalproposal of Belloni and Chernozhukov (2011) for the $\\ell_{1}$-penalizedquantile regression estimator. As an application, we analyze high dimensionaladditive quantile ...
Sparse penalized quantile regression is a useful tool for variable selection, robust estimation, and heteroscedasticity detection in high-dimensional data analysis. The computational issue of the sparse penalized quantile regression has not yet been fully resolved in the literature, due to nonsmoothness ...
The sparsity and bias of the lasso selection in high-dimensional linear regression. Ann. Stat. 36, 1567–1594 (2008). Article Google Scholar Javanmard, A. & Montanari, A. Model selection for high-dimensional regression under the generalized irrepresentability condition. Proc. of the 26th ...
Robust sparse Bayesian regression for longitudinal gene–environment interactions doi:10.1093/jrsssc/qlaf027longitudinal gene–environment interactionMCMC (Markov Chain Monte Carlo)quantile mixed-effects modelrobust Bayesian variable selectionstructured spike-and-slab priorsIn longitudinal studies, repeated measure...
Predictivity: assessed through the AUROC for classification tasks or the RMSE for regression tasks Model performances were evaluated over 100 random repetitions using a repeated five-fold or Monte Carlo CV strategy. Sparse, reliable biomarker discovery from single-omic data ...
(2020), which applies similar square loss transformation to non-smooth quantile loss in a non-Byzantine setup. Their converging rate (Theorem 2) is of the same order as (24) of the smooth loss but not the rate in (27) of the non-smooth loss. We believe the inconsistency comes from ...
This paper proposes a shape-restricted nonparametric quantile regression to estimate the 蟿-frontier, which acts as a benchmark for whether a decision maki... Y Wang,S Wang,C Dang,... - 《European Journal of Operational Research》 被引量: 17发表: 2014年 Joint estimation of multiple networks...
Extended Data Figure 3 Manhattan and quantile quantile plots for melancholia. a, Manhattan plot of GWAS for melancholia using the MLMe method implemented in FastLMM on 9,846 samples (4,509 cases, 5,337 controls). b, Quantile–quantile plot of GWAS for melancholia; λ = 1.069, λ 1000...
The dots give the average values over the 100 simulation runs, the error bars extend from the 5% to the 95% quantile. The dashed line represents the 13 A robust knockoff filter for sparse regression analysis of… no contamination 10% cont. 20% cont. 281 CKF RCKFcl RCKFrob no ...