A test for the mean vector with fewer observations than the dimension

Authors:
Muni S. Srivastava;Meng Du
Affiliations:
Department of Statistics, University of Toronto, Toronto, Canada M5S 3G3;Department of Statistics, University of Toronto, Toronto, Canada M5S 3G3
Venue:
Journal of Multivariate Analysis
Year:
2008

Citing 0
Cited 8

Estimation of the precision matrix of a singular Wishart distribution and its application in high-dimensional data

Journal of Multivariate Analysis
Analysis of high-dimensional repeated measures designs: The one sample case

Computational Statistics & Data Analysis
A test for the mean vector with fewer observations than the dimension under non-normality

Journal of Multivariate Analysis
Variable selection by Cp statistic in multiple responses regression with fewer sample size than the dimension

KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
A two sample test in high dimensional data

Journal of Multivariate Analysis
Tests for multivariate analysis of variance in high dimension under non-normality

Journal of Multivariate Analysis
Asymptotic distributions of some test criteria for the mean vector with fewer observations than the dimension

Journal of Multivariate Analysis
A high-dimensional two-sample test for the mean using random subspaces

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we consider a test for the mean vector of independent and identically distributed multivariate normal random vectors where the dimension p is larger than or equal to the number of observations N. This test is invariant under scalar transformations of each component of the random vector. Theories and simulation results show that the proposed test is superior to other two tests available in the literature. Interest in such significance test for high-dimensional data is motivated by DNA microarrays. However, the methodology is valid for any application which involves high-dimensional data.