Kato. Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models. ArXiv e-prints, 2013.Alexandre Belloni, Victor Chernozhukov, and Kengo Kato. Valid post-selection inference in high- dimensional approximately sparse quantile regression models. Journal of the American...
An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression Source: Journal of Computational and Graphical Statistics Asymptotically Minimax Adaptive Estimation. I: Upper Bounds. Optimally Adaptive Estimates Source: Theory of Probability and Its Applications ...
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 ...
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 ...
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 ...
From a methodological standpoint, we associate severe variation with the lower tails of the shocks hitting the target variable and thus adopt a quantile regression framework to predict a lower quantile of the target variable, using the TASSYRI lagged level as a predictor. Consistently with ...
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...
For finite samples with binary outcomes penalized logistic regression such as ridge logistic regression has the potential of achieving smaller mean squared errors (MSE) of coefficients and predictions than maximum likelihood estimation. There is evidence, however, that ridge logistic regression can result...
(2020). Distributed high-dimensional regression under a quantile loss function. The Journal of Machine Learning Research, 21(182), 1–43. MathSciNet MATH Google Scholar Chen, Y., Su, L., & Xu, J. (2017). Distributed statistical machine learning in adversarial settings. Proceedings of the...
Simultaneous multiple non-crossing quantile regression estimation using kernel constraints Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many application... Y Liu,Y Wu - 《Journal of Nonparametric Statistics》 被...