Journal of the Korean Statistical Society

  • 제25권1호
  • /
  • Pages.81-94
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  • 1996
  • /
  • 1226-3192(pISSN)
  • /
  • 2005-2863(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지

Bayesian Hypothesis Testing in Multivariate Growth Curve Model.

  • Kim, Hea-Jung (Department of Statistics, Dongguk University, Seoul, 100-715) ;
  • Lee, Seung-Joo (Department of Applied Statistics, Chong-Ju University, Chong-Ju, 360-764)
  • 발행 : 1996.03.01
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초록

This paper suggests a new criterion for testing the general linear hypothesis about coefficients in multivariate growth curve model. It is developed from a Bayesian point of view using the highest posterior density region methodology. Likelihood ratio test criterion(LRTC) by Khatri(1966) results as an approximate special case. It is shown that under the simple case of vague prior distribution for the multivariate normal parameters a LRTC-like criterion results; but the degrees of freedom are lower, so the suggested test criterion yields more conservative test than is warranted by the classical LRTC, a result analogous to that of Berger and Sellke(1987). Moreover, more general(non-vague) prior distributions will generate a richer class of tests than were previously available.

키워드

참고문헌

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