Journal of applied mathematics & informatics

한국전산응용수학회 (The Korean Society for Computational and Applied Mathematics)
권호 표지

MULTIVARIATE JOINT NORMAL LIKELIHOOD DISTANCE

  • 발행 : 2009.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The likelihood distance for the joint distribution of two multivariate normal distributions with common covariance matrix is explicitly derived. It is useful for identifying outliers which do not follow the joint multivariate normal distribution with common covariance matrix. The likelihood distance derived here is a good ground for the use of a generalized Wilks statistic in influence analysis of two multivariate normal data.

키워드

참고문헌

  1. K.-M. Jung, M. G. Kim and B. C. Kim, Second order local influence in linear discriminant analysis, J. Jpn. Soc. Comp. Statist., 10 (1997), 1-11
  2. K.-M. Jung and M. G. Kim, Influence analysis in selecting discriminant variables, J. Korean Statist. Soc., 30 (2001), 499-509
  3. M. G. Kim, The influence in comparing covariance matrices, Commun. Statist.- Theory Meth., 24 (1995), 1431-1441. https://doi.org/10.1080/03610929508831563
  4. M. G. Kim, Case influence on multiple correlation coefficient, J. Appl. Math. & Computing, 19 (2005), 521-525
  5. M. G. Kim, Influence on Hotelling's test statistic, J. Korean Data Anal. Soc., 9 (2007), 545-551.
  6. K. V. Mardia, J. T. Kent and J. M. Bibby, Multivariate Analysis, Academic Press, 1979
  7. S. S. Wilks, Multivariate statistical outliers, Sankhyfia, 25 (1963), 407-426