An unknown signal plus white noise is observed at n discrete time points. Within a large convex class of linear estimators of , we choose the estimator b that minimizes estimated quadratic risk. By construction, b is nonlinear. This estimation is done after orthogonal transformation of the data to a reasonable coordinate system. The procedure adaptively tapers the coeecients of the transformed ...

In this paper, we are concerned with regression problems where covariates can be grouped in nonoverlapping blocks, and where only a few of them are assumed to be active. In such a situation, the group Lasso is an attractive method for variable selection since it promotes sparsity of the groups. We study the sensitivity of any group Lasso solution to the observations and provide its precise loca...

The question of recovering a multiband signal from noisy observations motivates a model in which the multivariate data points consist of an unknown deter-ministic trend observed with multivariate Gaussian errors. A cognate random trend model suggests aane shrinkage estimators ^ A and ^ B for , which are related to an extended Efron-Morris estimator. When represented canonically, ^ A performs co...

The problem of estimating the coeecients in a linear regression model is considered when some of the response values are missing. The conventional Yates procedure employing least squares predictions for missing values does not lead to any improvement over the least squares estimator using complete observations only. However, if we use Stein-rule predictions , it is demonstrated that some improv...

We present a procedure for effective estimation of entropy and mutual information from smallsample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically as well as computationally. Despite its simplicity, we show that it outperform...

among 2379 patients with upper urinary tract stones who underwent eswl (extracorporeal shock wave lithotripsy) at sina hospitál using the siemens lithostar, 638 developed stone street (steinstrasse). of these, 516 (81%) passed all of the stone fragments spontaneously and no treatment was required, 90 cases (14%) had more eswl sessions to complete the treatment, and 30 (4.7%) required further in...

Hierarchical models are extensively studied and widely used in statistics and many other scientific areas. They provide an effective tool for combining information from similar resources and achieving partial pooling of inference. Since the seminal work by James and Stein (1961) and Stein (1962), shrinkage estimation has become one major focus for hierarchical models. For the homoscedastic norm...

Smooth James-Stein thresholding-based estimators enjoy smoothness like ridge regression and perform variable selection like lasso. They have added flexibility thanks to more than one regularization parameters (like adaptive lasso), and the ability to select these parameters well thanks to a unbiased and smooth estimation of the risk. The motivation is a gravitational wave burst detection proble...

Hans C. van Houwelingen Saskia le Cessie Department of Medical Statistics, Leiden, The Netherlands P.O.Box 9604 2300 RC Leiden, The Netherlands email: [email protected] Abstract A review is given of shrinkage and penalization as tools to improve predictive accuracy of regression models. The James-Stein estimator is taken as starting point. Procedures covered are the Pre-test Estimation, ...

The aim of this paper is to assess the performance of the Markowitz meanvariance framework over a thirty year time frame and address the question of; How should an investor optimally allocate their capital?. The effect of risk reduction by incorporating a Bayes-Stein estimator is also investigated. The performance of the framework is concluded by the out-ofsample performance of the mean-varianc...