Which power of goodness of fit tests can really be expected: intermediate versus contiguous alternatives
Janssen Arnold
Statistics & Risk Modeling, 2003, vol. 21, issue 4, 301-326
Abstract:
The present paper studies envelope power functions for goodness of fit models for asuitable submodel of infinite dimension of all continuous distributions on the real line. It turns out that after rescaling our alternatives with the factor 1/√n various envelope power bounds hold uniformly w.r.t. sample size n. The two-sided Neyman-Pearson power, the maximin and optimum mean power bounds of dimension d are studied in detail. It is shown that the latter envelope power bounds become flat for high dimensions d of alternative if they are compared with the power of Neyman-Pearson tests which serve as benchmark. These results can be used to compare intermediate and Pitman efficiency of goodness of fit tests. It is pointed out that contiguous alternatives can be used to discriminate competing tests whereas the intermediate efficiency is some sort of consistency only. It is also pointed out that no overall superior adaptive goodness of fit test exist. Agood comparison of competing tests can be done by their level points. It is shown that the level points of the maximin tests of dimension d grow with the rate d1/4.
Date: 2003
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://doi.org/10.1524/stnd.21.4.301.25350 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
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:bpj:strimo:v:21:y:2003:i:4/2003:p:301-326:n:2
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/strm/html
DOI: 10.1524/stnd.21.4.301.25350
Access Statistics for this article
Statistics & Risk Modeling is currently edited by Robert Stelzer
More articles in Statistics & Risk Modeling from De Gruyter
Bibliographic data for series maintained by Peter Golla ().