Asymptotic Normmality of Maximum Likelihood Estimators Obtained from Normally Distributed but Dependent Observations
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In this article we aim to establish intuitively appealing and verifiable conditions for the first-order efficiency and asymptotic normality of ML estimators in a multi-parameter framework, assuming joint normality but neither the independence nor the identical distribution of the observations. We present five theorems (and a large number of lemmas and propositions), each being a special case of its predecessor.
1986 ◽
Vol 40
(3)
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pp. 169-188
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1996 ◽
Vol 17
(2)
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pp. 233-238
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1997 ◽
Vol 60
(1)
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pp. 69-76
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1986 ◽
Vol 4
(6)
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pp. 309-311
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1981 ◽
Vol 30
(1-2)
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pp. 13-22
1999 ◽
Vol 20
(2)
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pp. 309-312
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