Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Asymptotic Test for Dimensionality in Probabilistic Principal Component Analysis with Missing Values

  • Published : 2004.04.01
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Abstract

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

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References

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