Richard D. Gill

TL;DR: An efficiency theorem is proved which shows that such an estimator asymptotically achieves the information bound in the generalized Hajek-Le Cam convolution and asymPTotic local minimax theorems.

TL;DR: In this article, a Bayesian version of the Cramer-Rao lower bound due to van trees was used to give an elementary proof that the limiting distibution of any regular estimator cannot have a variance less than the classical information bound, under minimal regularity conditions.

TL;DR: In this article, it was shown that a compactly differentiable generalized likelihood estimator asymptotically achieves the information bound in the generalized Hajek-Le Cam convolution and local minimax theorems.

TL;DR: The notion of coarsening at random (CAR) was introduced by Heitjan and Rubin (1991) to describe the most general form of randomly grouped, censored, or missing data, for which the CAR mechanism can be ignored when making likelihood-based inference about the parameters of the distribution of the variable of interest as discussed by the authors.