Economics at your fingertips  

Some high-dimensional one-sample tests based on functions of interpoint distances

Enakshi Saha, Soham Sarkar and Anil K. Ghosh

Journal of Multivariate Analysis, 2017, vol. 161, issue C, 83-95

Abstract: The multivariate one-sample location problem is well studied in the literature, and several tests are available for it. But most of the existing one-sample tests perform poorly for high-dimensional data, and many of them are not even applicable when the dimension of the data exceeds the sample size. In this article, we develop and investigate some nonparametric one-sample tests based on functions of interpoint distances. These proposed tests can be conveniently used in high dimension, low sample size (HDLSS) situations, and good power properties of these tests for HDLSS data have been established using theoretical as well as numerical results.

Keywords: High-dimensional consistency; HDLSS data; Rotation invariance; Scale invariance (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
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:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2018-11-10
Handle: RePEc:eee:jmvana:v:161:y:2017:i:c:p:83-95