Authors

Danny D. Dyer

Document Type

Report

Source Publication Title

Technical Report 77

Abstract

Let [see pdf for notation] be a family of probability density functions indexed by the parameter [see pdf for notation]. We assume at least one of the [see pdf for notation] is unknown. Based on a random sample of size n from [see pdf for notation], let [see pdf for notation] be two point estimators of the real-valued function [see pdf for notation], where [see pdf for notation] are specified constants, if any. When comparing [see pdf for notation] and [see pdf for notation], it is quite common to examine the ratio of their respective average precisions usually measured by either mean squared error, [see pdf for notation], or mean absolute error, [see pdf for notation], where [see pdf for notation]. If, for example, [see pdf for notation] for some w0' then 02 is said to be more mean squared efficient than [see pdf for notation] at [see pdf for notation]. However, the numerical value of such a ratio provides very limited insight into the actual relative behavior of the two competing estimators. We, therefore, propose a twofold technique for comparing [see pdf for notation] and which essentially determines (a) the "odds" in favor of [see pdf for notation] being closer to [see pdf for notation] than is [see pdf for notation] and (b) the average closeness of [see pdf for notation] to [see pdf for notation] not only when [see pdf for notation] is closer to [see pdf for notation] than is [see pdf for notation] but also when it is not. Closeness to [see pdf for notation] is measured through an absolute error loss function: [see pdf for notation]. Furthermore, joint consideration of these two concepts is shown to provide a basis for determining which of the two estimators, [see pdf for notation] or [see pdf for notation], is preferred in a given situation. An application of these results will be made with regard to the comparison of estimators of certain reliability characteristics in the exponential failure model.

Disciplines

Mathematics | Physical Sciences and Mathematics

Publication Date

3-1-1978

Language

English

Included in

Mathematics Commons

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.