搜索结果: 1-6 共查到“high-dimensional linear models”相关记录6条 . 查询时间(0.093 秒)
Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
2012/9/21
Residual variance and the signal-to-noise ratio are important quantities in many statistical models and model fitting procedures. They play an important role in regression diagnostics, in determining ...
Grouping Strategies and Thresholding for High Dimensional Linear Models
Structured sparsity Grouping, Learning Theory Non Linear Methods Block-thresholding coherence Wavelets
2012/7/9
The estimation problem in a high regression model with structured sparsity is investigated.An algorithm using a two steps block thresholding procedure called GR-LOL is provided.Convergence rates are p...
Estimation in high-dimensional linear models with deterministic design matrices
Identifiability projection ridge regression sparsity thresholding variable selection
2012/6/5
Because of the advance in technologies, modern statistical studies often encounter linear models with the number of explanatory variables much larger than the sample size. Estimation and variable sele...
Shrinkage estimators for out-of-sample prediction in high-dimensional linear models
high-dimensional linear model out-of-sample estimators
2011/2/15
We study the unconditional out-of-sample prediction error (predictive risk) associated with two classes of smooth shrinkage estimators for the linear model: James-Stein type shrinkage estimators and r...
Shrinkage estimators for out-of-sample prediction in high-dimensional linear models
Shrinkage estimators for out-of-sample high-dimensional linear models
2011/2/15
We study the unconditional out-of-sample prediction error (predictive risk) associated with two classes of smooth shrinkage estimators for the linear model: James-Stein type shrinkage estimators and r...
On universal oracle inequalities related to high-dimensional linear models
On universal oracle inequalities high-dimensional linear models
2010/11/1
This paper deals with recovering an unknown vector $\theta$ from the noisy data $Y=A\theta+\sigma\xi$, where $A$ is a known $(m\times n)$-matrix and $\xi$ is a white Gaussian noise. It is assumed tha...