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Multivariate Regression Shrinkage and Selection by Canonical Correlation Analysis
Adaptive Lasso Canonical Correlation Analysis Multivariate Regression
2016/1/25
The problem of regression shrinkage and selection for multivariate regression is considered. The goal is to consistently identify those variables relevant for regression. This is done not only for pre...
A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems
A General Iterative Shrinkage Thresholding Algorithm Non-convex Regularized Optimization Problems
2013/5/2
Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterp...
This manuscript shows that AdaBoost and its immediate variants can produce approximate maximum margin classifiers simply by scaling step size choices with a fixed small constant. In this way, when the...
Shrinkage estimators for prediction out-of-sample: Conditional performance
James-Stein estimator rando mmatrix theory random design
2012/11/22
We find that, in a linear model, the James-Stein estimator, which dominates the maximum-likelihood estimator in terms of its in-sample prediction error, can perform poorly compared to the maximum-like...
Geometric sensitivity of random matrix results: consequences for shrinkage estimators of covariance and related statistical methods
random matrix related statistical shrinkage estimators
2011/6/16
Shrinkage estimators of covariance are an important tool in modern applied and theoretical statistics.
They play a key role in regularized estimation problems, such as ridge regression (aka Tykhonov
...
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...
Local shrinkage rules, Lévy processes, and regularized regression
Local shrinkage rules Lévy processes regularized regression
2010/10/19
We use L\'evy processes to generate joint prior distributions for a location parameter $\bbeta = (\beta_1,...,\beta_p) $ as $p$ grows large. This leads to the class of local-global shrinkage rules. We...
A Class of Shrinkage Estimators for Variance of a Normal Population
Shrinkage Estimators Variance a Normal Population
2009/9/17
A Class of Shrinkage Estimators for Variance of a Normal Population。
Structural shrinkage of nonparametric spectral estimators for multivariate time series
structural shrinkage nonparametric spectral estimators multivariate time series
2009/9/16
In this paper we investigate the performance of periodogram based estimators of the spectral density matrix of possibly high-dimensional time series. We suggest and study shrinkage as a remedy against...
Thresholding-based iterative selection procedures for model selection and shrinkage
Sparsity nonconvex penalties thresholding model selection & shrinkage lasso ridge SCAD
2009/9/16
This paper discusses a class of thresholding-based iterative selection procedures (TISP) for model selection and shrinkage. People have long before noticed the weakness of the convex $l_1$-constraint ...
Bayesian shrinkage prediction for the regression problem
Bayesian prediction shrinkage estimation Normal regression superharmonic function minimaxity Kullback-Leibler divergence
2010/4/26
We consider Bayesian shrinkage predictions for the Normal regression problem
under the frequentist Kullback-Leibler risk function.
Firstly, we consider the multivariate Normal model with an unknown ...