Invariance in linear estimators

Ridge Stochastic Restricted Estimators in Semiparametric Linear Measurement Error Models

In this article we consider the stochastic restricted ridge estimation in semipara-metric linear models when the covariates are measured with additive errors. The development of penalized corrected likelihood method in such model is the basis for derivation of ridge estimates. The asymptotic normality of the resulting estimates are established. Also, necessary and sufficient condition...

Stein Type Estimators for Disturbance Variance in Linear Regression Model

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Thresholding Estimators for Linear

Computing Linear Mini-max Estimators

Consider a vector of data y = + "; y 2 IR n , where " = (" i) 1in is an error vector which satisses the "usual\ conditions, and 2 IR n. The problem is to nd/compute a vector m for which hm;yi minimizes the maximal risk among all linear estimators, i.e. m is a solution of min m2IR n max 2 E h`;i ? hm;i 2 , ` 2 IR n given. A solution of this mini-max problem is determined by the solution of the f...

Thresholding projection estimators in functional linear models

We consider the problem of estimating the regression function in functional linear regression models by proposing a new type of projection estimators which combine dimension reduction and thresholding. The introduction of a threshold rule allows to get consistency under broad assumptions as well as minimax rates of convergence under additional regularity hypotheses. We also consider the particu...