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A nonlinear perturbation theory for estimation and control of time discrete stochastic systems
H.W. Sorenson
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MULTI-ΒAYESIAN ESTIMATION THEORY
Statistics & Risk Modeling, 1986This paper presents a general theory of multi-Bayesian estimation. The authors consider a class of non-randomized procedures D which is essentially B-complete (equivalent to essential completeness in Wald's theory). Through a series of lemmas and theorems, the authors give necessary and sufficient conditions for D to be essentially B-complete.
de Waal, D. J. +3 more
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2020
Estimation theory is a branch of statistics that deals with estimating interested parameters from random observations (or sampled data). An estimator (also called estimation rules), defining the corresponding rules of inference, can be classified into point and interval estimators.
Dayi Wang +3 more
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Estimation theory is a branch of statistics that deals with estimating interested parameters from random observations (or sampled data). An estimator (also called estimation rules), defining the corresponding rules of inference, can be classified into point and interval estimators.
Dayi Wang +3 more
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Multiterminal estimation theory
IEEE/CAM Information Theory Workshop at Cornell, 2005The basic questions here are how to construct effective encoders @,Q2 and the related estimators 6 for the parameter 0; what is the minimum variance of these estimators ?; and what is the maximum Fisher information attainable under the rate constraints RI .R2 for the Shannon information ?. etc.
null To Sun Han, S. Amari
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2015
We know from our basic knowledge of statistics that one of the objectives in statistics is to better understand and model the underlying process which generates data. This is known as statistical inference: we infer from information contained in sample properties of the population from which the observations are taken.
Wolfgang Karl Härdle, Zdeněk Hlávka
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We know from our basic knowledge of statistics that one of the objectives in statistics is to better understand and model the underlying process which generates data. This is known as statistical inference: we infer from information contained in sample properties of the population from which the observations are taken.
Wolfgang Karl Härdle, Zdeněk Hlávka
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1974
We first superficially sketch the problem we will treat in this chapter. In the previous chapter we dealt with the question of how one can acquire more precise information on the value of an unknown parameter on the basis of a sample. Although one tries to construct confidence sets which are “as small as possible”, one cannot be guided in such a ...
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We first superficially sketch the problem we will treat in this chapter. In the previous chapter we dealt with the question of how one can acquire more precise information on the value of an unknown parameter on the basis of a sample. Although one tries to construct confidence sets which are “as small as possible”, one cannot be guided in such a ...
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Journal of the Royal Statistical Society. Series A (General), 1976
R. R. Harris, Demetrios G. Lainiotis
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R. R. Harris, Demetrios G. Lainiotis
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