A test for equality of two distributions via jackknife empirical likelihood and characteristic functions

Zhi Liu, Xiaochao Xia and Wang Zhou

Computational Statistics & Data Analysis, 2015, vol. 92, issue C, 97-114

Abstract: The two-sample problem: testing the equality of two distributions is investigated. A jackknife empirical likelihood (JEL) test is proposed through incorporating characteristic functions, which reduces to a two-sample U-statistic. When the dimension of data is fixed, the nonparametric Wilks’s theorem for the proposed JEL ratio statistics is established. When the dimension diverges with the sample size at a moderate rate, p=o(n1/3), it is proved that under some mild conditions the normalized JEL ratio statistic has a standard normal limit. Moreover, when the dimension exceeds the sample size, p>n, an alternative version of JEL test is proposed. It is verified that under the null hypothesis this alternative version of JEL test has an asymptotical chi-squared distribution with two degrees of freedom. Some numerical results via simulation study and an analysis of a microarray dataset are presented and both confirm theoretical results empirically.

Keywords: Jackknife empirical likelihood; Two-sample test; Equality of distributions; Characteristic function; Normal limit (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947315001425
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:csdana:v:92:y:2015:i:c:p:97-114

DOI: 10.1016/j.csda.2015.06.004

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().