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统计推断原理(英文版) |
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统计推断原理(英文版) |
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基本信息·出版社：人民邮电出版社
·页码：219 页
·出版日期：2009年08月
·ISBN：7115210748/9787115210746
·条形码：9787115210746
·版本：第1版
·装帧：平装
·开本：16
·正文语种：英语
·丛书名：图灵原版数字·统计学系列
·外文书名：Principles of Statistical Inference
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。
媒体推荐 “这是伟大统计学家的伟大著作。千万不能错过！
——Ronaid Christensen。Journal of the American StatisticaI Association
“本书是现代统计学之父的力作，深入阐述了统计推断的内容，行文流畅、语言优美。对所有从事统计工作的人来说，本书不可不读。”
——Davtd Hand（伦敦大学帝国学院）
“非常优秀的一本教材，在频率学派和贝叶斯学派之间找到了绝好的平衡，给出不偏不倚的观点。”
——《应用统计》杂志
编辑推荐 《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。
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
……
序言 Most statistical work is concerned directly with the provision and implementa-tion of methods for study design and for the analysis and interpretation of data.The theory of statistics deals in principle with the general concepts underlyingall aspects of such work and from this perspective the formal theory of statisticalinference is but a part of that full theory. Indeed, from the viewpoint of indi-vidual applications, it may seem rather a small part. Concern is likely to be moreconcentrated on whether models have been reasonably formulated to addressthe most fruitful questions, on whether the data are subject to unappreciatederrors or contamination and, especially, on the subject-matter interpretation ofthe analysis and its relation with other knowledge of the field.
Yet the formal theory is important for a number of reasons. Without somesystematic structure statistical methods for the analysis of data become a col-lection of tricks that are hard to assimilate and interrelate to one another, orfor that matter to teach. The development of new methods appropriate for newproblems would become entirely a matter of ad hoc ingenuity. Of course suchingenuity is not to be undervalued and indeed one role of theory is to assimilate,generalize and perhaps modify and improve the fruits of such ingenuity.
Much of the theory is concerned with indicating the uncertainty involved inthe conclusions of statistical analyses, and with assessing the relative merits ofdifferent methods of analysis, and it is important even at a very applied level tohave some understanding of the strengths and limitations of such discussions.This is connected with somewhat more philosophical issues connected withthe nature of probability. A final reason, and a very good one, for study of thetheory is that it is interesting.
The object of the present book is to set out as compactly as possible thekey ideas of the subject, in particular aiming to describe and compare the mainideas and controversies over more foundational issues that have rumbled on atvarying levels of intensity for more than 200 years.
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