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

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.

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Year of publication:	1975
Authors:	Michel, R.
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 5.1975, 1, p. 67-82
Publisher:	Elsevier
Keywords:	Asymptotic maximum likelihood estimators of a vector parameter asymptotic expansion of asymptotic maximum likelihood estimators Edgeworth expansion convex sets

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

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