| 词条 | 统计推断原理(英文版) |
| 释义 | 本书是统计学名家名作，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。本书可作为工科、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。 图书信息书名: 统计推断原理(英文版) 外文书名: Principles of Statistical Inference 作者：（英）考克斯 出版社:人民邮电出版社; 第1版 (2009年8月1日) 丛书名: 图灵原版数字·统计学系列 平装:219页 正文语种:英语 开本:16 定价: 49.00元 ISBN:9787115210746 条形码:9787115210746 内容简介《统计推断原理(英文版)》是在现代统计学之父Cox授课讲义内容的基础上成形的，系统地介绍了统计推断的理论，既涵盖了传统的频率统计学。又囊括了现代的贝叶斯统计学。除介绍了统计推断的重要概念如参数。似然、主元等之外。还阐述了显著性检验。渐进理论以及较复杂的统计推断问题，并特别介绍了实验设计中基于随机化的统计推断。对于核心概念的解释非常清晰，读者即使跳过其中的数学细节，也能理解有关概念。 作者简介考克斯 (Cox.D.R.) ，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。 图书目录1 Preliminaries Summary 1.1 Starting point 1.2 Role of formal theory of inference 1.3 Some simple models 1.4 Formulation of objectives 1.5 Two broad approaches to statistical inference 1.6 Some further discussion 1.7 Parameters Notes 1 2 Some concepts and simple applications Summary 2.1 Likelihood 2.2 Sufficiency 2.3 Exponential family 2.4 Choice of priors for exponential family problems 2.5 Simple frequentist discussion 2.6 Pivots Notes 2 3 Significance tests Summary 3.1 General remarks 3.2 Simple significance test 3.3 One- and two-sided tests 3.4 Relation with acceptance and rejection 3.5 Formulation of alternatives and test statistics 3.6 Relation with interval estimation 3.7 Interpretation of significance tests 3.8 Bayesian testing Notes 3 4 More complicated situations Summary 4.1 General remarks 4.2 General Bayesian formulation 4.3 Frequentist analysis 4.4 Some more general frequentist developments 4.5 Some further Bayesian examples Notes 4 5 Interpretations of uncertainty Summary 5.1 General remarks 5.2 Broad roles of probability 5.3 Frequentist interpretation of upper limits 5.4 Neyman-Pearson operational criteria 5.5 Some general aspects of the frequentist approach 5.6 Yet more on the frequentist approach 5.7 Personalistic probability 5.8 Impersonal degree of belief 5.9 Reference priors 5.10 Temporal coherency 5.11 Degree of belief and frequency 5.12 Statistical implementation of Bayesian analysis 5.13 Model uncertainty 5.14 Consistency of data and prior 5.15 Relevance of frequentist assessment 5.16 Sequential stopping 5.17 A simple classification problem Notes 5 6 Asymptotic theory Summary 6.1 General remarks 6.2 Scalar parameter 6.3 Multidimensional parameter 6.4 Nuisance parameters 6.5 Tests and model reduction 6.6 Comparative discussion 6.7 Profile likelihood as an information summarizer 6.8 Constrained estimation 6.9 Semi-asymptotic arguments 6.10 Numerical-analytic aspects 6.11 Higher-order asymptotics Notes 6 7 Further aspects of maximum likelihood Summary 7.1 Multimodal likelihoods 7.2 Irregular form 7.3 Singular information matrix 7.4 Failure of model 7.5 Unusual parameter space 7.6 Modified likelihoods Notes 7 8 Additional objectives Summary 8.1 Prediction 8.2 Decision analysis 8.3 Point estimation 8.4 Non-likelihood-based methods Notes 8 9 Randomization-based analysis Summary 9.1 General remarks 9.2 Sampling a finite population 9.3 Design of experiments Notes 9 Appendix A: A brief history Appendix B: A personal view References Author index Subject index |
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