WebDriven by a wide range of applications, high-dimensional linear regression, where the dimension p can be much larger than the sample size n, has received significant recent attention. The linear model is (1.1) y =Xβ +ε, ε∼N 0,σ2I, where y ∈Rn, X∈Rn×p and β ∈Rp. Several penalized/constrained 1 minimiza- WebIn the case of high dimensional spaces, the vectors become uniformly distant from each other, the ratio between the nearest and farthest vector approaches 1. ... We could observe the performance of each method in the Linear Regression and Friedman dataset using between 50 and 1000 features in Figure 4.

High-dimensional regression - Carnegie Mellon University

Web29 de nov. de 2010 · Consistent group selection in high-dimensional linear regression. Fengrong Wei, Jian Huang. In regression problems where covariates can be naturally … WebGuo et al.: Spline-Lasso in High-Dimensional Linear Regression 289 Fused Lasso Estimation (MSE 1.996806e+001) 300 location Spline MCP Estimation (MSE 1.424764e+001) 6 ο 0 100 200 300 400 500 600 location Figure 1. Estimation results for X ~ 7V(0, Σ )where Σ,·,· = 1 and sent the estimation for each method. The upper figure is the … theo urbach logo

Randomized tests for high-dimensional regression: more efficient …

WebTransfer learning in high-dimensional regression 5 els simultaneously. The multi-task learning considered in Lounici et al. (2009) estimates multiple high-dimensional sparse linear models under the assumption that the supports of all the regression coe cients are the same. In multi-task learning, di erent regularization formats have been WebThis paper considers estimation and prediction of a high-dimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, … WebDownloadable (with restrictions)! High-dimensional data are nowadays readily available and increasingly common in various fields of empirical economics. This article considers … theourgia