Titlebook: Differential-Geometrical Methods in Statistics; Shun-ichi Amari Book 1985 Springer-Verlag Berlin Heidelberg 1985 Estimator.probability.pro

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书目名称Differential-Geometrical Methods in Statistics影响因子(影响力)




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Curved Exponential Families and Edgeworth Expansions or distribution in S. In chapter, we decompose . into a pair (û, v) of statistics such that û is asymptotically sufficient and v is asymptotically ancillary. The Edeworth expansion of the joint distribution p (û, v) is given explicitly up to the third order terms by using the related geometrical quantities in S and M.

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Restricted Geometric Relationshipsial geometry. The dualistic structure of the geometry is elucidated by using the α-flat manifold will turn out to be an interesting generalization of the Euclidean space, admitting the Pythagorean relation with respect to the α-divergence of two points.

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Enhancing The Quality of Retrieval Methods or distribution in S. In chapter, we decompose . into a pair (û, v) of statistics such that û is asymptotically sufficient and v is asymptotically ancillary. The Edeworth expansion of the joint distribution p (û, v) is given explicitly up to the third order terms by using the related geometrical quantities in S and M.

elucidate

Eliminating Selves, Reducing Personsrms of the covariance of an efficient estimators are decomposed into the sum of three non-negative geometrical terms. This proves that the bias corrected maximum likelihood estimator is the best estimator from the point of view of the third order asymptotic evaluation. The effect of parametrization is elucidated from the geometrical viewpoint.

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https://doi.org/10.1007/978-3-031-13995-6ests, not depending on a specific model M. We also give the characteristics of the conditional test conditioned on the asymptotic ancillary. The third-order characteristics of interval estimators are also shown. For the sake of simplicity, we maily treat a one-dimensional model, and the multi-dimensional generalization is explained shortly.

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