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Essentials of Statistical Inference (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 16) 🔍
Young, G. A., G. A. Young, R. L. Smith Cambridge : Cambridge University Press, 2005., Cambridge Series in Statistical and Probabilistic Mathematics 16, 1, 2005
英语 [en] · PDF · 3.1MB · 2005 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/zlib · Save
描述
PrefaceThis book aims to provide a concise but comprehensive account of the essential elements ofstatistical inference and theory. It is designed to be used as a text for courses on statisticaltheory for students of mathematics or statistics at the advanced undergraduate or Masterslevel (UK) or the first-year graduate level (US), or as a reference for researchers in otherfields seeking a concise treatment of the key concepts of and approaches to statisticalinference. It is intended to give a contemporary and accessible account of procedures usedto draw formal inference from data.The book focusses on a clear presentation of the main concepts and results underlyingdifferent frameworks of inference, with particular emphasis on the contrasts amongfrequentist, Fisherian and Bayesian approaches. It provides a description of basic materialon these main approaches to inference, as well as more advanced material on recentdevelopments in statistical theory, including higher-order likelihood inference, bootstrapmethods, conditional inference and predictive inference. It places particular emphasis oncontemporary computational ideas, such as applied in bootstrap methodology and Markovchain Monte Carlo techniques of Bayesian inference. Throughout, the text concentrateson concepts, rather than mathematical detail, but every effort has been made to presentthe key theoretical results in as precise and rigorous a manner as possible, consistent withthe overall mathematical level of the book. The book contains numerous extended examplesof application of contrasting inference techniques to real data, as well as selectedhistorical commentaries. Each chapter concludes with an accessible set of problems andexercises.Prerequisites for the book are calculus, linear algebra and some knowledge of basicprobability (including ideas such as conditional probability, transformations of densitiesetc., though not measure theory). Some previous familiarity with the objectives of andmain approaches to statistical inference is helpful, but not essential. Key mathematical andprobabilistic ideas are reviewed in the text where appropriate.
备用文件名
lgrsnf/D:/!genesis/library.nu/11/_261113.1140acc1f32e1dd8400ebc947c568a09.pdf
备用文件名
nexusstc/Essentials of Statistical Inference/1140acc1f32e1dd8400ebc947c568a09.pdf
备用文件名
zlib/Science (General)/G. A. Young, R. L. Smith/Essentials of Statistical Inference_999783.pdf
备选标题
Essentials of statistical inference : G.A. Young, R.L. Smith
备选标题
Essentials_of_statistical_inference_a02
备选作者
Young, G. A., R. L. Smith, G. A. Young
备选作者
G. A. Young, R. L. Smith, Young, G. A
备选作者
G. A. Young, R. L. Smith, G.A YOUNG
备选作者
Young, G. A., Smith, R. L.
备选作者
G A Young; Richard L Smith
备选作者
G. Alastair Young
备用出版商
Cambridge University Press (Virtual Publishing)
备用出版商
Greenwich Medical Media Ltd
备用版本
Cambridge series in statistical and probabilistic mathematics, Cambridge series on statistical and probabilistic mathematics, 1st pbk. ed., Cambridge, UK, England, 2010
备用版本
Cambridge series in statistical and probabilistic mathematics, Cambridge series on statistical and probabilistic mathematics, Cambridge, UK, New York, England, 2005
备用版本
Cambridge series on statistical and probabilistic mathematics, 16, Cambridge, UK ; New York, 2005
备用版本
CAMBRIDGE SERIES IN STATISTICAL AND PROBABILISTIC MATHEMATICS; 16, NEW YORK, Unknown
备用版本
Cambridge University Press, Cambridge, 2005
备用版本
United Kingdom and Ireland, United Kingdom
备用版本
Illustrated, 1, PT, 2010
备用版本
July 25, 2005
元数据中的注释
до 2011-01
元数据中的注释
lg575513
元数据中的注释
{"edition":"1","isbns":["0511126166","0511755392","0521548667","0521839718","9780511126161","9780511755392","9780521548663","9780521839716"],"last_page":238,"publisher":"Cambridge University Press","series":"Cambridge Series in Statistical and Probabilistic Mathematics 16"}
元数据中的注释
Includes bibliographical references (p. [218]-222) and index.
元数据中的注释
Includes bibliographical references (p. [218-222) and index.
备用描述
Aimed At Advanced Undergraduate And Graduate Students In Mathematics And Related Disciplines, This Book Presents The Concepts And Results Underlying The Bayesian, Frequentist And Fisherian Approaches, With Particular Emphasis On The Contrasts Between Them. Computational Ideas Are Explained, As Well As Basic Mathematical Theory. Written In A Lucid And Informal Style, This Concise Text Provides Both Basic Material On The Main Approaches To Inference, As Well As More Advanced Material On Developments In Statistical Theory, Including: Material On Bayesian Computation, Such As Mcmc, Higher-order Likelihood Theory, Predictive Inference, Bootstrap Methods And Conditional Inference. It Contains Numerous Extended Examples Of The Application Of Formal Inference Techniques To Real Data, As Well As Historical Commentary On The Development Of The Subject. Throughout, The Text Concentrates On Concepts, Rather Than Mathematical Detail, While Maintaining Appropriate Levels Of Formality. Each Chapter Ends With A Set Of Accessible Problems. G. A. Young, R. L. Smith. Title From Publisher's Bibliographic System (viewed On 01 Jun 2016). Mode Of Access: World Wide Web.
备用描述
This engaging textbook presents the concepts and results underlying the Bayesian, frequentist and Fisherian approaches to statistical inference, with particular emphasis on the contrasts between them. Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, it covers in a concise treatment both basic mathematical theory and more advanced material, including such contemporary topics as Bayesian computation, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference. It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject. Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Each chapter ends with a set of accessible problems. Some prior knowledge of probability is assumed, while some previous knowledge of the objectives and main approaches to statistical inference would be helpful but is not essential.
备用描述
"Written in an informal style, this concise text provides both basic material on the main approaches to inference, as well as more advanced material on modern developments in statistical theory, including: contemporary material on Bayesian computation, such as MCMC, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference. It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject. Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Each chapter ends with a set of accessible problems." "Based to a large extent on lectures given at the University of Cambridge over a number of years, the material has been polished by student feedback. Some prior knowledge of probability is assumed, while some previous knowledge of the objectives and main approaches to statistical inference would be helpful but is not essential."--BOOK JACKET
备用描述
This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference, with particular emphasis on the contrasts between them. Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, it covers basic mathematical theory as well as more advanced material, including such contemporary topics as Bayesian computation, higher-order likelihood theory, predictive inference, bootstrap methods, and conditional inference.
备用描述
Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, this engaging textbook gives a concise account of the main approaches to inference, with particular emphasis on the contrasts between them. It is the first textbook to synthesize contemporary material on computational topics with basic mathematical theory
备用描述
<p><p>concise Account Of Main Approaches; First Textbook To Synthesize Modern Computation With Basic Theory.</p>
开源日期
2011-06-04
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