RESEARCH COMMONS
LIBRARY

Comparison of Point Estimators of Normal Percentiles

ResearchCommons/Manakin Repository

Comparison of Point Estimators of Normal Percentiles

Show simple item record

dc.contributor.author Dyer, Danny D. en
dc.contributor.author Hensley, Onas L. en
dc.contributor.author Keating, Jerome P. en
dc.date.accessioned 2010-06-03T16:08:58Z en
dc.date.available 2010-06-03T16:08:58Z en
dc.date.issued 1977-08 en
dc.identifier.uri http://hdl.handle.net/10106/2296 en
dc.description.abstract There are available several point estimators of the percentiles of a normal distribution with both mean and variance unknown. Consequently, it would seam appropriate to make a comparison among the estimators through sums "closeness to the true value" criteria. Along these lines, the concept of Pitman-closeness efficiency is introduced. Essentially, when comparing two estimators, the Pitman-closeness efficiency gives "odds" in favor of one of the estimators being closer to the true value than is the other in a given situation. Through the use of Pitman-closeness efficiency, this paper compares (a) the maximum likelihood estimator, (b) the minimum variance unbissed estimator, (c) the best invariant estimator, and (d) the median unbiased estimator within a class of estimators which includes (a), (b), and (c). Mean squared efficiency is also discussed. en
dc.language.iso en_US en
dc.publisher University of Texas at Arlington en
dc.relation.ispartofseries Technical Report;67 en
dc.subject Fatigue life en
dc.subject Pitman-closeness efficiency en
dc.subject Mean squared efficiency en
dc.subject Point estimators en
dc.subject.lcsh Statistics en
dc.subject.lcsh Mathematics Research en
dc.title Comparison of Point Estimators of Normal Percentiles en
dc.type Technical Report en
dc.publisher.department Department of Mathematics en

Files in this item

Files Size Format View Description
MathTechReport067.pdf 778.7Kb PDF View/Open PDF

This item appears in the following Collection(s)

Show simple item record

Browse

My Account

Statistics

About Us