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Titlebook: Statistical Inference in Science; D. A. Sprott Book 2000 Springer Science+Business Media New York 2000 Likelihood.mathematical statistics.

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發(fā)表于 2025-3-21 18:40:54 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Statistical Inference in Science
編輯D. A. Sprott
視頻videohttp://file.papertrans.cn/877/876443/876443.mp4
叢書名稱Springer Series in Statistics
圖書封面Titlebook: Statistical Inference in Science;  D. A. Sprott Book 2000 Springer Science+Business Media New York 2000 Likelihood.mathematical statistics.
描述The aim of this book is to develop an understanding and treatment of the problems of inference associated with experiments in science. Many textbooks treat inference as principally the reduction of the sample information to estimates and their marginal distribution and supposedly optimal properties. In contrast, this book emphasizes techniques for dividing the sample information into various parts addressing the diverse problems of inference that arise from repeatable experiments. An unusually valuable feature of the book is the large number of practical examples, many of which use data taken from experiments published in various scientific journals. This book has evolved from the author‘s courses on statistical inference. It would be a suitable text book for advanced undergraduate, Masters, and Ph.D. statistics students. It can also be used as a reference book. A background knowledge of an introductory course in probability is assumed, including the calculation and manipulation of probability functions and density functions, transformation of variables and the use of Jacobians. The author is a Distinguished Professor Emeritus of Statistics, University of Waterloo, and Professor of
出版日期Book 2000
關(guān)鍵詞Likelihood; mathematical statistics; probability; statistical inference; statistics
版次1
doihttps://doi.org/10.1007/b98955
isbn_softcover978-1-4757-7396-5
isbn_ebook978-0-387-22766-5Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpringer Science+Business Media New York 2000
The information of publication is updating

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The Likelihood Function,egarded as coming from a hypothetical infinite population of possible observations having probability function .(.,θ) depending on an unknown parameter θ. The inferential structure of the model is fully specified by the three elements: the sample space . = ., the parameter space Ω = θ, and the proba
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Division of Sample Information II: Likelihood Structure, a single scalar parameter or a vector parameter of two components. In the former case likelihood functions can be plotted and likelihood intervals, or the union of such intervals, obtained. In the latter case likelihood contours can be plotted and likelihood regions obtained. The difficulty in deal
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0172-7397 textbooks treat inference as principally the reduction of the sample information to estimates and their marginal distribution and supposedly optimal properties. In contrast, this book emphasizes techniques for dividing the sample information into various parts addressing the diverse problems of infe
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