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An asymptotic expansion for the distribution of asymptotic maximum likelihood estimators of vector parameters

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  • Michel, R.

Abstract

It is shown that the probability that a suitably standardized asymptotic maximum likelihood estimator of a vector parameter (i.e., an estimator which approximates the solution of the likelihood equation in a reasonably good way) lies in a measurable convex set can be approximated by an integral involving a multidimensional normal density function and a series in n-1/2 with certain polynomials as coefficients.

Suggested Citation

  • Michel, R., 1975. "An asymptotic expansion for the distribution of asymptotic maximum likelihood estimators of vector parameters," Journal of Multivariate Analysis, Elsevier, vol. 5(1), pages 67-82, March.
    • Handle: RePEc:eee:jmvana:v:5:y:1975:i:1:p:67-82
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    Cited by:

    1. Kakizawa, Yoshihide, 2010. "Comparison of Bartlett-type adjusted tests in the multiparameter case," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1638-1655, August.

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