Combining dependent tests based on data depth with applications to the two-sample problem for data of arbitrary types

Linli Tang and Jun Li

Journal of Nonparametric Statistics, 2022, vol. 34, issue 1, 113-140

Abstract: Combining multiple tests has many real world applications. However, most existing methods fail to directly take into account the underlying dependency among the tests. In this paper, we propose a novel procedure to combine dependent tests based on the notion of data depth. The proposed method can automatically incorporate the underlying dependency among the tests, and is nonparametric and completely data-driven. To demonstrate its application, we apply the proposed combining method to develop a new two-sample test for data of arbitrary types when the data can be metrizable and their information can be characterised by interpoint distances. Our simulation studies and real data analysis show that the proposed test based on the new combining method performs well across a broad range of settings and compares favourably with existing tests.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2021.2025371 (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:taf:gnstxx:v:34:y:2022:i:1:p:113-140

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2021.2025371

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().