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A note on the existence of maximum likelihood estimates for Gaussian-inverted Wishart models

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  • Le, N.
  • Sun, L.
  • Zidek, J. V.

Abstract

We show that a non-identifiability problem can occur when one attempts to estimate the degrees of freedom and the unstructured covariance matrix simultaneously in a Gaussian-inverted Wishart model, using the maximum likelihood approach. However, the EM algorithm may falsely converge to give finite estimates in this case simply because of the convergence criterion used. An alternative approach is proposed to overcome the problem.

Suggested Citation

  • Le, N. & Sun, L. & Zidek, J. V., 1998. "A note on the existence of maximum likelihood estimates for Gaussian-inverted Wishart models," Statistics & Probability Letters, Elsevier, vol. 40(2), pages 133-137, September.
    • Handle: RePEc:eee:stapro:v:40:y:1998:i:2:p:133-137
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    References listed on IDEAS

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    1. Nhu D. Le & Weimin Sun & James V. Zidek, 1997. "Bayesian Multivariate Spatial Interpolation with Data Missing by Design," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(2), pages 501-510.
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    Cited by:

    1. Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.

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