Rohana J. Karunamuni

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The empirical Bayes approach to multiple decision problems with a sequential decision problem as the component is studied. An empirical Bayes m-truncated sequential decision procedure is exhibited for general multiple decision problems. With a sequential component, an empirical Bayes sequential decision procedure selects both a stopping rule function and a(More)
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)