On Some Asymptotic Properties of Maximum Likelihood Estimates and Related Bayes' Estimates PDF Download
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Author: Lucien LeCam
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ISBN:
Category : Mathematical statistics
Languages : en
Pages : 0
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Author: Lucien LeCam
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 0
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Author: Lucien Marie Le Cam
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ISBN:
Category : Asymptotic expansions
Languages : en
Pages : 64
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Author: Lucien Le Cam
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Category :
Languages : fr
Pages :
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Author: Stanford University. Department of Statistics
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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: S. R. Chakravorti
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Category :
Languages : en
Pages : 12
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Author: Billy Joe Attebery
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Category :
Languages : en
Pages : 132
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Author: Douglas H. Jones
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Category :
Languages : en
Pages : 38
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The progress of modern mental test theory depends very much on the techniques of maximum likelihood estimation, and many popular applications make use of likelihoods induced by logistic item response models. While, in reality, item responses are nonreplicate within a single examinee and the logistic models are only ideal, practitioners make inferences using the asymptotic distribution of the maximum likelihood estimator derived as if item responses were replicated and satisfied their ideal model. This article proposes a sample space acknowledging these two realities and derives the asymptotic distribution of the induced maximum likelihood estimator. This article assumes that items, while sampled from an infinite set of items, have but a finite domain of alternate response functions: this situation is the case of the finite-generic-item-pool. Using the proposed sample space, the article applies the statistical functional approach of von Mises to derive the influence curve of the maximum likelihood estimator; to discuss related robustness properties; and to derive new classes of resistent estimators. The aim is revealing the value of these methods for uncovering the relative merits of different item response functions. Proofs and mathematical derivations are minimized to increase the accessability of this complex subject. (Author).
Author: Robert Ernest Tarone
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ISBN:
Category :
Languages : en
Pages : 190
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Author: Erling Bernhard Andersen
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Category :
Languages : en
Pages : 34
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Author: Pranab Kumar Sen
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Category :
Languages : en
Pages : 24
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