搜索结果: 1-12 共查到“管理学 Shrinkage”相关记录12条 . 查询时间(0.062 秒)
Multivariate Regression Shrinkage and Selection by Canonical Correlation Analysis
Adaptive Lasso Canonical Correlation Analysis Multivariate Regression
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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
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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...
Margins, Shrinkage, and Boosting
Margins Shrinkage Boosting
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2013/5/2
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
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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
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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
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Shrinkage estimators for out-of-sample prediction in high-dimensional linear models
high-dimensional linear model out-of-sample estimators
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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
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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
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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
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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
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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
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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
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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 ...