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Akaike Information Criterion for Selecting Components of the Mean Vector in High Dimensional Data with Fewer Observations

Author

Listed:
  • Muni S. Srivastava

    (Department of Statistics, University of Toronoto)

  • Tatsuya Kubokawa

    (Faculty of Economics, University of Tokyo)

Abstract

The Akaike information criterion (AIC) has been used very successfully in the literature in model selection for small number of parameters pand large number of observations N. The cases when pis large and close to N or when p>N have not been considered in the literature. In fact, when pis large and close to N, the available AIC does not perform well at all. We consider these cases in the context of finding the number of components of the mean vector that may be different from zero in one-sample multivariate analysis. In fact, we consider this problem in more generality by considering it as a growth curve model introduced in Rao (1959) and Potthoff and Roy (1964). Using simulation, it has been shown that the proposed AIC procedures perform very well.

Suggested Citation

  • Muni S. Srivastava & Tatsuya Kubokawa, 2007. "Akaike Information Criterion for Selecting Components of the Mean Vector in High Dimensional Data with Fewer Observations," CIRJE F-Series CIRJE-F-472, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2007cf472
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