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[英] 考克斯 著 / 人民邮电出版社 / 2009-08 / 平装
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统计推断原理
《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。
1PreliminariesSummary1.1Startingpoint1.2Roleofformaltheoryofinference1.3Somesimplemodels1.4Formulationofobjectives1.5Twobroadapproachestostatisticalinference1.6Somefurtherdiscussion1.7ParametersNotes12SomeconceptsandsimpleapplicationsSummary2.1Likelihood2.2Sufficiency2.3Exponentialfamily2.4Choiceofpriorsforexponentialfamilyproblems2.5Simplefrequentistdiscussion2.6PivotsNotes23SignificancetestsSummary3.1Generalremarks3.2Simplesignificancetest3.3One-andtwo-sidedtests3.4Relationwithacceptanceandrejection3.5Formulationofalternativesandteststatistics3.6Relationwithintervalestimation3.7Interpretationofsignificancetests3.8BayesiantestingNotes34MorecomplicatedsituationsSummary4.1Generalremarks4.2GeneralBayesianformulation4.3Frequentistanalysis4.4Somemoregeneralfrequentistdevelopments4.5SomefurtherBayesianexamplesNotes45InterpretationsofuncertaintySummary5.1Generalremarks5.2Broadrolesofprobability5.3Frequentistinterpretationofupperlimits5.4Neyman-Pearsonoperationalcriteria5.5Somegeneralaspectsofthefrequentistapproach5.6Yetmoreonthefrequentistapproach5.7Personalisticprobability5.8Impersonaldegreeofbelief5.9Referencepriors5.10Temporalcoherency5.11Degreeofbeliefandfrequency5.12StatisticalimplementationofBayesiananalysis5.13Modeluncertainty5.14Consistencyofdataandprior5.15Relevanceoffrequentistassessment5.16Sequentialstopping5.17AsimpleclassificationproblemNotes56AsymptotictheorySummary6.1Generalremarks6.2Scalarparameter6.3Multidimensionalparameter6.4Nuisanceparameters6.5Testsandmodelreduction6.6Comparativediscussion6.7Profilelikelihoodasaninformationsummarizer6.8Constrainedestimation6.9Semi-asymptoticarguments6.10Numerical-analyticaspects6.11Higher-orderasymptoticsNotes67FurtheraspectsofmaximumlikelihoodSummary7.1Multimodallikelihoods7.2Irregularform7.3Singularinformationmatrix7.4Failureofmodel7.5Unusualparameterspace7.6ModifiedlikelihoodsNotes78AdditionalobjectivesSummary8.1Prediction8.2Decisionanalysis8.3Pointestimation8.4Non-likelihood-basedmethodsNotes89Randomization-basedanalysisSummary9.1Generalremarks9.2Samplingafinitepopulation9.3DesignofexperimentsNotes9AppendixA:AbriefhistoryAppendixB:ApersonalviewReferencesAuthorindexSubjectindex
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