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雷奧奇·卡塞拉、羅傑L.貝耶編著的《統計推斷（英文版原書第2版）》從概率論的基礎開始，通過例子與習題的旁徵博引，引進瞭大量近代統計處理的新技術和一些國內同類教材中不能見而廣為使用的分布。其內容包括工科概率論入門、經典統計和現代統計的基礎，又加進瞭不少近代統計中數據處理的實用方法和思想，例如：Bootstrap再抽樣法、刀切（Jackknife）估計、EM算法、Logistic迴歸、穩健（Robust）迴歸、Markov鏈、Monte Carlo方法等。它的統計內容與國內流行的教材相比，理論較深，模型較多，案例的涉及麵要廣，理論的應用麵要豐富，統計思想的闡述與算法更為具體。《統計推斷（英文版原書第2版）》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。

齣版說明
序
1 Probability Theory
1.1 Set Theory
1.2 Basics of Probability Theory
1.2.1 Axiomatic Foundations
1.2.2 The Calculus of Probabilities
1.2.3 Counting
1.2.4 Enumerating Outcomes
1.3 Conditional Probability and Independence
1.4 Random Variables
1.5 Distribution Functions
1.6 Density and Mass Functions
1.7 Exercises
1.8 Miscellanea
2 Transformations and Expectations
2.1 Distributions of Functions of a Random Variable
2.2 Expected Values
2.3 Moments and Moment Generating Functions
2.4 Differentiating Under an Integral Sign
2.5 Exercises
2.6 Miscellanea
3 Common Families of Distributions
3.1 Introduction
3.2 Discrete Distributions
3.3 Continuous Distributions
3.4 Exponential Families
3.5 Location and Scale Families
3.6 Inequalities and Identities
3.6.1 Probability Inequalities
3.6.2 Identities
3.7 Exercises
3.8 Miscellanea
4 Multiple Random Variables
4.1 Joint and Marginal Distributions
4.2 Conditional Distributions and Independence
4.3 Bivariate Transformations
4.4 Hierarchical Models and Mixture Distributions
4.5 Covariance and Correlation
4.6 Multivariate Distributions
4.7 Inequalities
4.7.1 Numerical Inequalities
4.7.2 Functional Inequalities
4.8 Exercises
4.9 Miscellanea
5 Properties of a Random Sample
5.1 Basic Concepts of Random Samples
5.2 Sums of Random Variables from a Random Sample
5.3 Sampling from the Normal Distribution
5.3.1 Properties of the Sample Mean and Variance
5.3.2 The Derived Distributions: Student's t and Snedecor's F
5.4 Order Statistics
5.5 Convergence Concepts
5.5.1 Convergence in Probability
5.5.2 Almost Sure Convergence
5.5.3 Convergence in Distribution
5.5.4 The Delta Method
5.6 Generating a Random Sample
5.6.1 Direct Methods
5.6.2 Indirect Methods
5.6.3 The Accept/Reject Algorithm
5.7 Exercises
5.8 Miscellanea
6 Principles of Data Reduction
6.1 Introduction
6.2 The Sufficiency Principle
6.2.1 Sufficient Statistics
6.2.2 Minimal Sufficient Statistics
6.2.3 Ancillary Statistics
6.2.4 Sufficient, Ancillary, and Complete Statistics
……
7 Point Estimation
8 Hypothesis Testing
8.1 Introduction
9 Interval Estimation
10 Asymptotic Evaluations
11 Analysis of Variance and Regression
12 Regression Models
Appendix: Computer Algebra
Table of Common Distributions
References
Author Index
Subject Index
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