In a linear regression model with homoscedastic Normal noise, I consider James–Stein type shrinkage in the estimation of nuisance parameters associated with control variables. For at least three control variables and exogenous treatment, I show that the standard leastsquares estimator is dominated with respect to squared-error loss in the treatment effect even among unbiased estimators and even...

this article examines statistical inference for  where and are independent but not identically distributed pareto of the first kind (pareto (i)) random variables with same scale parameter but different shape parameters. the maximum likelihood, uniformly minimum variance unbiased and bayes estimators with gamma prior are used for this purpose. simulation studies which compare the estimators are ...

the problem of estimating the parameter ?, when it is restricted to an interval of the form , in a class of discrete distributions, including binomial negative binomial discrete weibull and etc., is considered. we give necessary and sufficient conditions for which the bayes estimator of with respect to a two points boundary supported prior is minimax under squared log error loss function. for s...

We propose an adaptive shrinkage estimator for use in regression problems charaterized by many predictors, such as wavelet estimation. Adaptive estimators perform well over a variety of circumstances, such as regression models in which few, some or many coefficients are zero. Our estimator, PolyShrink, adaptively varies the amount of shrinkage to suit the estimation task. Whereas hard threshold...

We consider the problem of estimating the scale parameter &beta of a rescaled F-distribution when &beta has a lower bounded constraint of the form &beta&gea, under the entropy loss function. An admissible minimax estimator of the scale parameter &beta, which is the pointwise limit of a sequence of Bayes estimators, is given. Also in the class of truncated linear estimators, the admissible estim...

Inverse sampling design is generally considered to be appropriate technique when the population is divided into two subpopulations, one of which contains only few units. In this paper, we derive the Horvitz-Thompson estimator for the population mean under inverse sampling designs, where subpopulation sizes are known. We then introduce an alternative unbiased estimator, corresponding to post-st...

This paper aims to study the empirical Bayes estimation of the parameter of ЭРланга distribution under a weighted squared error loss function. Bayes estimator is firstly to derive based on pivot method. Then empirical Bayes estimator of unknown parameter is constructed in a priori unknown circumstances. The asymptotically optimal property of this empirical Bayes estimator is also discussed. It ...