Estimating a linear parametric function of a doubly censored exponential distribution

Abstract

ABSTRACTFor an arbitrary strictly convex loss function, we study the problem of estimating a linear parametric function is a known constant, when a doubly censored sample is available from a two-parameter exponential population. We establish the inadmissibility of the best affine equivariant (BAE) estimator by deriving an improved estimator. We provide various implications for quadratic and linex loss functions in detail. Improvements are obtained for the absolute value loss function as well. Further a new class of estimators improving upon the BAE estimator is derived using the Kubokawa method. This class is shown to include some benchmark estimators from the literature.