统计推断原理

作者: [英] 考克斯著

出版社: 人民邮电出版社

出版时间: 2009-08

版次: 1

ISBN: 9787115210746

定价: 49.00

装帧: 平装

开本: 16开

纸张: 胶版纸

页数: 219页

字数: 232千字

正文语种: 英语

丛书: 图灵原版数学·统计学系列

分类: 自然科学

1 张插图图片

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　　《统计推断原理(英文版)》是统计学名家名作，包含9章内容和两个附录，前面几章介绍一些基本概念，如参数、似然、主元等，然后介绍显著性检验、渐进理论以及比较复杂的统计推断问题。还特别介绍了实验设计中基于随机化的统计推断。核心概念的解释非常清晰，即使跳过其中的数学细节，也能使读者理解。
　　《统计推断原理(英文版)》可作为工科、管理类学科专业本科生、研究生的教材或参考书，也可供教师、工程技术人员自学之用。　　D.R.Cox，世界著名统计学家，英国皇家学会会员暨英国社会科学院院士，美国科学院、丹麦皇家科学院外籍院士。曾任国际统计协会、伯努利数理统汁与概率学会、英国皇家统计学会主席。主要学术贡献包括Cox过程和影响深远且应用广泛的Cox比例风险模型等。 1Preliminaries
Summary
1.1Startingpoint
1.2Roleofformaltheoryofinference
1.3Somesimplemodels
1.4Formulationofobjectives
1.5Twobroadapproachestostatisticalinference
1.6Somefurtherdiscussion
1.7Parameters
Notes1

2Someconceptsandsimpleapplications
Summary
2.1Likelihood
2.2Sufficiency
2.3Exponentialfamily
2.4Choiceofpriorsforexponentialfamilyproblems
2.5Simplefrequentistdiscussion
2.6Pivots
Notes2

3Significancetests
Summary
3.1Generalremarks
3.2Simplesignificancetest
3.3One-andtwo-sidedtests
3.4Relationwithacceptanceandrejection
3.5Formulationofalternativesandteststatistics
3.6Relationwithintervalestimation
3.7Interpretationofsignificancetests
3.8Bayesiantesting
Notes3

4Morecomplicatedsituations
Summary
4.1Generalremarks
4.2GeneralBayesianformulation
4.3Frequentistanalysis
4.4Somemoregeneralfrequentistdevelopments
4.5SomefurtherBayesianexamples
Notes4

5Interpretationsofuncertainty
Summary
5.1Generalremarks
5.2Broadrolesofprobability
5.3Frequentistinterpretationofupperlimits
5.4Neyman-Pearsonoperationalcriteria
5.5Somegeneralaspectsofthefrequentistapproach
5.6Yetmoreonthefrequentistapproach
5.7Personalisticprobability
5.8Impersonaldegreeofbelief
5.9Referencepriors
5.10Temporalcoherency
5.11Degreeofbeliefandfrequency
5.12StatisticalimplementationofBayesiananalysis
5.13Modeluncertainty
5.14Consistencyofdataandprior
5.15Relevanceoffrequentistassessment
5.16Sequentialstopping
5.17Asimpleclassificationproblem
Notes5

6Asymptotictheory
Summary
6.1Generalremarks
6.2Scalarparameter
6.3Multidimensionalparameter
6.4Nuisanceparameters
6.5Testsandmodelreduction
6.6Comparativediscussion
6.7Profilelikelihoodasaninformationsummarizer
6.8Constrainedestimation
6.9Semi-asymptoticarguments
6.10Numerical-analyticaspects
6.11Higher-orderasymptotics
Notes6

7Furtheraspectsofmaximumlikelihood
Summary
7.1Multimodallikelihoods
7.2Irregularform
7.3Singularinformationmatrix
7.4Failureofmodel
7.5Unusualparameterspace
7.6Modifiedlikelihoods
Notes7

8Additionalobjectives
Summary
8.1Prediction
8.2Decisionanalysis
8.3Pointestimation
8.4Non-likelihood-basedmethods
Notes8

9Randomization-basedanalysis
Summary
9.1Generalremarks
9.2Samplingafinitepopulation
9.3Designofexperiments
Notes9

AppendixA：Abriefhistory
AppendixB：Apersonalview
References
Authorindex
Subjectindex
内容简介:
　　《统计推断原理(英文版)》是统计学名家名作，包含9章内容和两个附录，前面几章介绍一些基本概念，如参数、似然、主元等，然后介绍显著性检验、渐进理论以及比较复杂的统计推断问题。还特别介绍了实验设计中基于随机化的统计推断。核心概念的解释非常清晰，即使跳过其中的数学细节，也能使读者理解。
　　《统计推断原理(英文版)》可作为工科、管理类学科专业本科生、研究生的教材或参考书，也可供教师、工程技术人员自学之用。
作者简介:
　　D.R.Cox，世界著名统计学家，英国皇家学会会员暨英国社会科学院院士，美国科学院、丹麦皇家科学院外籍院士。曾任国际统计协会、伯努利数理统汁与概率学会、英国皇家统计学会主席。主要学术贡献包括Cox过程和影响深远且应用广泛的Cox比例风险模型等。
目录:
1Preliminaries
Summary
1.1Startingpoint
1.2Roleofformaltheoryofinference
1.3Somesimplemodels
1.4Formulationofobjectives
1.5Twobroadapproachestostatisticalinference
1.6Somefurtherdiscussion
1.7Parameters
Notes1

2Someconceptsandsimpleapplications
Summary
2.1Likelihood
2.2Sufficiency
2.3Exponentialfamily
2.4Choiceofpriorsforexponentialfamilyproblems
2.5Simplefrequentistdiscussion
2.6Pivots
Notes2

3Significancetests
Summary
3.1Generalremarks
3.2Simplesignificancetest
3.3One-andtwo-sidedtests
3.4Relationwithacceptanceandrejection
3.5Formulationofalternativesandteststatistics
3.6Relationwithintervalestimation
3.7Interpretationofsignificancetests
3.8Bayesiantesting
Notes3

4Morecomplicatedsituations
Summary
4.1Generalremarks
4.2GeneralBayesianformulation
4.3Frequentistanalysis
4.4Somemoregeneralfrequentistdevelopments
4.5SomefurtherBayesianexamples
Notes4

5Interpretationsofuncertainty
Summary
5.1Generalremarks
5.2Broadrolesofprobability
5.3Frequentistinterpretationofupperlimits
5.4Neyman-Pearsonoperationalcriteria
5.5Somegeneralaspectsofthefrequentistapproach
5.6Yetmoreonthefrequentistapproach
5.7Personalisticprobability
5.8Impersonaldegreeofbelief
5.9Referencepriors
5.10Temporalcoherency
5.11Degreeofbeliefandfrequency
5.12StatisticalimplementationofBayesiananalysis
5.13Modeluncertainty
5.14Consistencyofdataandprior
5.15Relevanceoffrequentistassessment
5.16Sequentialstopping
5.17Asimpleclassificationproblem
Notes5

6Asymptotictheory
Summary
6.1Generalremarks
6.2Scalarparameter
6.3Multidimensionalparameter
6.4Nuisanceparameters
6.5Testsandmodelreduction
6.6Comparativediscussion
6.7Profilelikelihoodasaninformationsummarizer
6.8Constrainedestimation
6.9Semi-asymptoticarguments
6.10Numerical-analyticaspects
6.11Higher-orderasymptotics
Notes6

7Furtheraspectsofmaximumlikelihood
Summary
7.1Multimodallikelihoods
7.2Irregularform
7.3Singularinformationmatrix
7.4Failureofmodel
7.5Unusualparameterspace
7.6Modifiedlikelihoods
Notes7

8Additionalobjectives
Summary
8.1Prediction
8.2Decisionanalysis
8.3Pointestimation
8.4Non-likelihood-basedmethods
Notes8

9Randomization-basedanalysis
Summary
9.1Generalremarks
9.2Samplingafinitepopulation
9.3Designofexperiments
Notes9

AppendixA：Abriefhistory
AppendixB：Apersonalview
References
Authorindex
Subjectindex

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统计推断原理

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