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

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發(fā)表于 2025-3-21 19:46:41 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Differential-Geometrical Methods in Statistics
編輯Shun-ichi Amari
視頻videohttp://file.papertrans.cn/279/278845/278845.mp4
叢書名稱Lecture Notes in Statistics
圖書封面Titlebook: Differential-Geometrical Methods in Statistics;  Shun-ichi Amari Book 1985 Springer-Verlag Berlin Heidelberg 1985 Estimator.probability.pro
描述.From the reviews:. "In this Lecture Note volume the author describes his differential-geometric approach to parametrical statistical problems summarizing the results he had published in a series of papers in the last five years. The author provides a geometric framework for a .special. class of test and estimation procedures for curved exponential families. ... ... The material and ideas presented in this volume are important and it is recommended to everybody interested in the connection between statistics and geometry ..." #.Metrika.#1 "More than hundred references are given showing the growing interest in differential geometry with respect to statistics. The book can only strongly be recommended to a geodesist since it offers many new insights into statistics on a familiar ground." #.Manuscripta Geodaetica.#2
出版日期Book 1985
關(guān)鍵詞Estimator; probability; probability distribution; statistical inference; statistical model; statistics
版次1
doihttps://doi.org/10.1007/978-1-4612-5056-2
isbn_softcover978-0-387-96056-2
isbn_ebook978-1-4612-5056-2Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag Berlin Heidelberg 1985
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:43:57 | 只看該作者
Curved Exponential Families and Edgeworth Expansions or distribution in S. In chapter, we decompose . into a pair (?, v) of statistics such that ? is asymptotically sufficient and v is asymptotically ancillary. The Edeworth expansion of the joint distribution p (?, v) is given explicitly up to the third order terms by using the related geometrical quantities in S and M.
板凳
發(fā)表于 2025-3-22 01:21:03 | 只看該作者
地板
發(fā)表于 2025-3-22 07:21:35 | 只看該作者
5#
發(fā)表于 2025-3-22 09:10:53 | 只看該作者
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.
6#
發(fā)表于 2025-3-22 16:20:02 | 只看該作者
Enhancing The Quality of Retrieval Methods or distribution in S. In chapter, we decompose . into a pair (?, v) of statistics such that ? is asymptotically sufficient and v is asymptotically ancillary. The Edeworth expansion of the joint distribution p (?, v) is given explicitly up to the third order terms by using the related geometrical quantities in S and M.
7#
發(fā)表于 2025-3-22 21:06:00 | 只看該作者
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.
8#
發(fā)表于 2025-3-22 21:21:18 | 只看該作者
9#
發(fā)表于 2025-3-23 01:31:17 | 只看該作者
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.
10#
發(fā)表于 2025-3-23 05:50:36 | 只看該作者
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