Some Information Inequalities for Statistical Inference
Abstract
In this paper, we first describe the generalized notion of Cramer-Rao lower bound obtained by Naudts (2004) using two families of probability density functions, the original model and an escort model. We reinterpret the results in Naudts (2004) from a statistical point of view and obtain some interesting examples in which this bound is attained. Further we obtain information inequalities which generalize the classical Bhattacharyya bounds in both regular and non-regular cases.
Keywords
Cite
@article{arxiv.1802.04483,
title = {Some Information Inequalities for Statistical Inference},
author = {Harsha K and Alladi Subramanyam},
journal= {arXiv preprint arXiv:1802.04483},
year = {2018}
}
Comments
Some of the contents of this paper is accepted for a contributed talk in The Ninth International Conference on Guided Self-Organisation (GSO-2018: Information Geometry and Statistical Physics to be held in Max Planck Institute for Mathematics in the Sciences,Leipzig, Germany during March 26 - 28, 2018