Rohana J. Karunamuni

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Minimum distance techniques have become increasingly important tools for solving statistical estimation and inference problems. In particular, the successful application of the Hellinger distance approach to fully parametric models is well known. The corresponding optimal estimators, known as minimum Hellinger distance estimators, achieve efficiency at the(More)
We investigate the empirical Bayes estimation problem of multivariate regression coefficients under squared error loss function. In particular, we consider the regression model Y = Xβ + ε, where Y is an m-vector of observations, X is a known m × k matrix, β is an unknown k-vector, and ε is an m-vector of unobservable random variables. The problem is squared(More)