本書是統計學名家名作，包含9章內容和兩個附錄，前面幾章介紹一些基本概念，如參數、似然、主元等，然後介紹顯著性檢驗、漸進理論以及比較複雜的統計推斷問題。還特別介紹了實驗設計中基於隨機化的統計推斷。核心概念的解釋非常清晰，即使跳過其中的數學細節，也能使讀者理解。本書可作為工科、管理類學科專業本科生、研究生的教材或參考書，也可供教師、工程技術人員自學之用。
基本介紹
- 書名：統計推斷原理（英文版）
- 作者：（英）考克斯 (Cox.D.R.)
- 原版名稱：Principles of Statistical Inference
- ISBN：9787115210746
- 頁數：219頁
- 定價：49.00元
- 出版社：人民郵電出版社
- 出版時間：2009年8月1日
- 裝幀：平裝
- 開本：16
- 正文語種：英語
內容簡介,作者簡介,圖書目錄,
內容簡介
《統計推斷原理(英文版)》是在現代統計學之父Cox授課講義內容的基礎上成形的，系統地介紹了統計推斷的理論，既涵蓋了傳統的頻率統計學。又囊括了現代的貝葉斯統計學。除介紹了統計推斷的重要概念如參數。似然、主元等之外。還闡述了顯著性檢驗。漸進理論以及較複雜的統計推斷問題，並特別介紹了實驗設計中基於隨機化的統計推斷。對於核心概念的解釋非常清晰，讀者即使跳過其中的數學細節，也能理解有關概念。
作者簡介
考克斯 (Cox.D.R.) ，世界著名統計學家，英國皇家學會會員暨英國社會科學院院士，美國科學院、丹麥皇家科學院外籍院士。曾任國際統計協會、伯努利數理統汁與機率學會、英國皇家統計學會主席。主要學術貢獻包括Cox過程和影響深遠且套用廣泛的Cox比例風險模型等。
圖書目錄
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
