DOI QR코드

DOI QR Code

Influence Measures for a Test Statistic on Independence of Two Random Vectors

  • Jung Kang-Mo (Department of Informatics & Statistics, Kunsan National University)
  • Published : 2005.12.01

Abstract

In statistical diagnostics a large number of influence measures have been proposed for identifying outliers and influential observations. However it seems to be few accounts of the influence diagnostics on test statistics. We study influence analysis on the likelihood ratio test statistic whether the two sets of variables are uncorrelated with one another or not. The influence of observations is measured using the case-deletion approach, the influence function. We compared the proposed influence measures through two illustrative examples.

Keywords

References

  1. Cook, R.D. (1986). Assessment of local influence (with discussions), Journal of the Royal Statistical Society B, 48, 133-169
  2. Cook, R.D. and Weisberg, S. (1982). Residuals and Irfluence in Regression, Chapman and Hall, New York
  3. Critchley, F. (1985). Influence in principal component analysis, Biometrika, 72, 627-636 https://doi.org/10.1093/biomet/72.3.627
  4. Fung, W.-K. (1993). Unmasking outliers and leverage points: A confirmation, Journal of the American Statistical Association, 88, 545-519
  5. Hampel, F. R (1974). The influence curve and its role in robust estimation, Journal of the American Statistical Association, 69, 383-393 https://doi.org/10.2307/2285666
  6. Jung, K.-M. (2001). Influence analysis on a test statistic in canonical correlation coefficients, The Korean Communications in Statistics, 8, 347-355
  7. Jung, K.-M. (2002). Influence function of the likelihood ratio test statistic for multivariate normal sample, Communications in Statistics - Theory and Methods, 31, 1273-1281 https://doi.org/10.1081/STA-120006068
  8. Lawrence, A. J. (1995). Deletion influence and masking in regression, Journal of the Royal Statistical Society B, 57, 181-189
  9. Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis, Academic Press, New York
  10. Rencher, A. C. (1995). Methods of Multivariate Analysis, John Wiley & Sons, New York