Asymptotic Normality of the Maximum Likelihood Estimate in the Independent Not Identically Distributed Case PDF Download
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Author: A. N. Philippou
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
In the paper, the authors assume the existence and consistency of the maximum likelihood estimate (MLE) in the independent not identically distributed (i.n.i.d.) case and the authors establish its asymptotic normality. The regularity conditions employed do not involve the third order derivatives of the underlying probability density functions (p.d.f.'s). (Author).
Author: A. N. Philippou
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
In the paper, the authors assume the existence and consistency of the maximum likelihood estimate (MLE) in the independent not identically distributed (i.n.i.d.) case and the authors establish its asymptotic normality. The regularity conditions employed do not involve the third order derivatives of the underlying probability density functions (p.d.f.'s). (Author).
Author: Andreas N. Philippou
Publisher:
ISBN:
Category : Asymptotic distribution (Probability theory)
Languages : en
Pages : 19
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Author: Andreas N. Philippou
Publisher:
ISBN:
Category :
Languages : en
Pages : 350
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Author: LIH-WEN HUANG
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ISBN:
Category :
Languages : en
Pages : 59
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Author: Andreas N. Philippou
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ISBN:
Category :
Languages : en
Pages : 37
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Author: Stanford University. Department of Statistics
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ISBN:
Category : Analysis of variance
Languages : en
Pages : 556
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The problem considered is the estimation of the parameters in the mixed model of the analysis of variance, assuming normality of the random effects and errors. Both asymptotic properties of such estimates as the size of the design increases and numerical procedures for their calculation are discussed. Estimation is carried out by the method of maximum likelihood. It is shown that there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal and asymptotically efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix as the size of the design increases. This is accomplished using a Taylor series expansion of the log-likelihood. (Modified author abstract).
Author: William Dollard Commins (Jr.)
Publisher:
ISBN:
Category : Asymptotes
Languages : en
Pages : 116
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Author: Robert Ernest Tarone
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
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Author: JunjirÅ Ogawa
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ISBN:
Category : Asymptotic distribution (Probability theory)
Languages : en
Pages : 74
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Author: Byeong U. Park
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 12
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