On generalized moment identity and its applications: a unified approach

Article Type

Research Article

Publication Title

Statistics

Abstract

In this paper, we obtain a generalized moment identity for the case when the distributions of the random variables are not necessarily purely discrete or absolutely continuous. The proposed identity is useful to find the generator which has been used for the approximation of distributions by Stein's method. Apparently, a new approach is discussed for the approximation of distributions by Stein's method. We bring the characterization based on the relationship between conditional expectations and hazard measure in our unified framework. As an application, a new lower bound to the mean-squared error is obtained and it is compared with Bayesian Cramer–Rao bound.

First Page

1149

Last Page

1160

DOI

10.1080/02331888.2015.1119150

Publication Date

9-2-2016

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