Quantcast

On asymptotically efficient statistical inference on a signal parameter

Research paper by Mikhail Ermakov

Indexed on: 23 Jan '15Published on: 23 Jan '15Published in: Mathematics - Statistics



Abstract

We consider the problems of confidence estimation and hypothesis testing on a parameter of signal observed in Gaussian white noise. For these problems we point out lower bounds of asymptotic efficiency in the zone of moderate deviation probabilities. These lower bounds are versions of local asymptotic minimax Hajek-Le Cam lower bound in estimation and the lower bound for Pitman efficiency in hypothesis testing. The lower bounds were obtained for both logarithmic and sharp asymptotic of moderate deviation probabilities.