Bootstrapping spatial median for location problems

Myoungshic Jhun, Seung Jun Shin

Research output: Contribution to journalArticle

Abstract

In multivariate location problems, the sample mean is most widely used, having various advantages. It is, however, very sensitive to outlying observations and inefficient for data from heavy tailed distributions. In this situation, the spatial median is more robust than the sample mean and could be a reasonable alternative. We reviewed several spatial median based testing methods for multivariate location and compared their significance level and power through Monte Carlo simulations. The results show that bootstrap method is efficient for the estimation of the covariance matrix of the sample spatial median. We also proposed bootstrap simultaneous confidence intervals based on the spatial median for multiple comparisons in the multi-sample case.

Original languageEnglish
Pages (from-to)2123-2133
Number of pages11
JournalCommunications in Statistics: Simulation and Computation
Volume38
Issue number10
DOIs
Publication statusPublished - 2009 Nov 1
Externally publishedYes

Fingerprint

Bootstrapping
Location Problem
Sample mean
Covariance matrix
Simultaneous Confidence Intervals
Bootstrap Confidence Intervals
Multiple Comparisons
Heavy-tailed Distribution
Significance level
Bootstrap Method
Testing
Monte Carlo Simulation
Alternatives
Monte Carlo simulation

Keywords

  • Bootstrap
  • Multivariate location
  • Simultaneous confidence interval
  • Spatial median

ASJC Scopus subject areas

  • Modelling and Simulation
  • Statistics and Probability

Cite this

Bootstrapping spatial median for location problems. / Jhun, Myoungshic; Shin, Seung Jun.

In: Communications in Statistics: Simulation and Computation, Vol. 38, No. 10, 01.11.2009, p. 2123-2133.

Research output: Contribution to journalArticle

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