On the asymptotic power of a goodness-of-fit test based on a cumulative Kullback–Leibler discrepancy

A. Contreras-Cristán, E. Gutiérrez-Peña and S.G. Walker

Statistics & Probability Letters, 2017, vol. 120, issue C, 118-125

Abstract: We discuss a goodness-of-fit test arising from information-theoretical considerations. We show that, for a simple null hypothesis, our test has superior asymptotic power compared to the Anderson–Darling test when the alternative lies in a certain large class of distribution functions.

Keywords: EDF statistic; Information measure; Power of a test (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715215301991
Full text for ScienceDirect subscribers only

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:eee:stapro:v:120:y:2017:i:c:p:118-125

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2016.09.023

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().