Penalized regression methods, such as L1 regularization, are routinely used in high-dimensional applications, and there is a rich literature on optimality properties under sparsity assumptions. In the Bayesian paradigm, sparsity is routinely induced through two-component mixture priors having a probability mass at zero, but such priors encounter daunting computational problems in high dimension...

recent tendencies in concrete technology have been towards to high- performance concrete with a low water-cement ratio. however, high performance concretes have some problems. one of the problems is early-age cracking due to autogenous shrinkage. this study presents the results of an experimental investigation carried out to evaluate the autogenous shrinkage of high-strength concrete. accordin...

A variety of shrinkage methods have been proposed for estimation of some unknown parameter by considering estimators based on a prior guess of the value of the parameter. We compare some of the shrunken estimators for the parameters)! and 6 of the exponential distribution through simulation. INTRODUCTION In the estimation of an unknown parameter there often exists some form of prior knowledge a...

Donoho and Johnstone’s (1994) WaveShrink procedure has proven valuable for signal de-noising and non-parametric regression. WaveShrink has very broad asymptotic near-optimality properties. In this paper, we introduce a new shrinkage scheme, firm, which generalizes the hard and soft shrinkage proposed by Donoho and Johnstone (1994). We derive minimax thresholds and provide formulas for computing...

This paper contains the research on a hybrid algorithm combining the Matching Pursuit (MP) and the wavelet shrinkage. In this algorithm, we propose to shrink the scalar product of the element which best correlates with the residue before modifying. The study concerns a broad family of shrinkage functions. Using weak properties of these shrinkage functions, we show that the algorithm converges t...

The estimation of the slope parameter of two linear regression models with normal errors are considered, when it is apriori suspected that the two lines are parallel. The uncertain prior information about the equality of slopes is presented by a null hypothesis and a coefficient of distrust on the null hypothesis is introduced. The unrestricted estimator (UE) based on the sample responses and s...

As high-dimensional data becomes ubiquitous, standard estimators of the population covariance matrix become difficult to use. Specifically, in the case where the number of samples is small (large p small n) the sample covariance matrix is not positive definite. In this paper we explore some recent estimators of sample covariance matrices in the large p, small n setting namely, shrinkage estimat...

In non-parametric function estimation, providing a confidence interval with the right coverage is a challenging problem. This is especially the case when the underlying function has a wide range of unknown degrees of smoothness. Here we propose two methods of constructing an average coverage confidence interval built from block shrinkage estimation methods. One is based on the James-Stein shrin...

Maintainer Danni Yu Depends R (>= 3.0), caTools, RColorBrewer Description The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The meth...