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INVITED PAPER MULTIVARIATE ANALYSIS FOR THE CASE WHEN THE DIMENSION IS LARGE COMPARED TO THE SAMPLE SIZE

Fujikoshi, Yasunori   (Department of Statistics, Graduate School of Science, Hiroshima UniversityUU0014184  );
  • 초록

    This paper is concerned with statistical methods for multivariate data when the number p of variables is large compared to the sample size n. Such data appear typically in analysis of DNA microarrays, curve data, financial data, etc. However, there is little statistical theory for high dimensional data. On the other hand, there are some asymptotic results under the assumption that both and p tend to $\infty$ , in some ratio p/n ${\rightarrow}$ c. The results suggest that the new asymptotic results are more useful and insightful than the classical large sample asymptotics. The main purpose of this paper is to review some asymptotic results for high dimensional statistics as well as classical statistics under a high dimensional asymptotic framework.


  • 주제어

    Asymptotic distribution .   discriminant functions .   eigenvalues .   high dimensional inference .   MANOVA tests .   tests for covariance matrices.  

  • 참고문헌 (29)

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