Titlebook: Statistical Estimation; Asymptotic Theory I. A. Ibragimov,R. Z. Has’minskii Book 1981 Springer Science+Business Media New York 1981 Asympto

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凶猛

Several Estimation Problems in a Gaussian White Noise,e likelihood ratio . (see formula (2.A.17)) and the normality of the random field ln . — these facts allow us to avoid many difficulties. At the same time, the model is sufficiently interesting since the signal observed may to a large extent depend arbitrarily on the parameter. Finally, this model i

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Several Estimation Problems in a Gaussian White Noise, or expanding parameter set Θ, since this case is of special importance in information theory. Next, in Sections 2 and 3 the case when the signal 5 depends on the parameter in a discontinuous manner will be considered. Finally, in Sections 4 and 5 examples of non-parametric problems are given.

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Local Asymptotic Normality of Families of Distributions,less of the relation between the observations which produced the given likelihood function. This chapter is devoted to an investigation of the conditions under which this property is valid for various models and to corollaries of this property.

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Properties of Estimators in the Regular Case,sent chapter is to prove the asymptotic efficiency of a maximum likelihood estimator and of a large class of Bayesian and generalized Bayesian estimators for regular families of experiments. Evidently, certain new restrictions on the families under consideration will be required.

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Introductionbodies and two or more molecules of Cl, or (b) physical contact between the C1r subunits and the complex formed between the C1q heads and the Fc portion of the immunoglobulins, or some other portion of the immunoglobulins.