Comparisons among some predictors of exponential distributions using Pitman closeness

Mohammad Raqab (), Jafar Ahmadi () and Byan Arabli

Computational Statistics, 2013, vol. 28, issue 5, 2349-2365

Abstract: The bias, mean squared error and likelihood optimality criteria are used frequently to compare estimators and predictors. Recently, the probability of nearness around a statistic (estimator or predictor) has received a considerable attention in the literature. In this article, we adopt the Pitman’s measure of closeness (PMC) as an optimality criterion to compare the maximum likelihood, best linear unbiased, best linear invariant, median unbiased and conditional median predictors of a future ordered statistic based on a type II censored sample from an exponential distribution with unknown scale parameter. Numerical computations of the PMC for all comparisons among these predictors are performed and presented. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Prediction; Pitman-closest; Exponential distribution; Type II censored data (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00180-013-0410-0 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:5:p:2349-2365

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-013-0410-0

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().