A test for the mean vector in large dimension and small samples

Junyong Park, Deepak Nag Ayyala

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

In this paper, we consider the problem of testing the mean vector in the multivariate setting where the dimension p is greater than the sample size n, namely a large p and small n problem. We propose a new scalar transform invariant test and show the asymptotic null distribution and power of the proposed test under weaker conditions than Srivastava (2009). We also present numerical studies including simulations and a real example of microarray data with comparison to existing tests developed for a large p and small n problem.

Original languageEnglish (US)
Pages (from-to)929-943
Number of pages15
JournalJournal of Statistical Planning and Inference
Volume143
Issue number5
DOIs
StatePublished - May 1 2013
Externally publishedYes

Fingerprint

Microarrays
Small Sample
Testing
Null Distribution
Microarray Data
Asymptotic distribution
Numerical Study
Sample Size
Scalar
Transform
Invariant
Small sample
Simulation

Keywords

  • Asymptotic distribution
  • High dimension
  • Scalar transform invariant test
  • Testing mean vector

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Statistics and Probability

Cite this

A test for the mean vector in large dimension and small samples. / Park, Junyong; Ayyala, Deepak Nag.

In: Journal of Statistical Planning and Inference, Vol. 143, No. 5, 01.05.2013, p. 929-943.

Research output: Contribution to journalArticle

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