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Residual variance and the signal-to-noise ratio in high-dimensional linear models
Asymptoticnormality,high-dimensionaldataanalysis Poincar!a inequality randommatrices residualvariance signal-to-noiseratio
2012/11/21
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
2012/9/19
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...
Shrinkage estimators for out-of-sample prediction in high-dimensional linear models
high-dimensional linear model out-of-sample estimators
2011/3/21
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
2011/3/23
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...