“本書提供了有關最新現(xiàn)代貝葉斯統(tǒng)計方法的重要題材，文筆流暢，語言優(yōu)美，其突出的特點是包括大量實際應用，涉及若干領域中AIC和BIC模型選擇標準的運用和對比，通過效用理論以獨特方式處理貝葉斯決策論，并論述了貝葉斯過程的頻度特性，配備了可以擴展與加深書中內(nèi)容的有趣和適中的自學練習?！薄狹ichael J．Evans，Mathematical Review“以嚴密、純熟的文筆介紹貝葉斯建模的基本原則，選材深思熟慮，按照研究生層次引入貝葉斯方法?！薄狫ournal of the American Statistical Association貝葉斯“后驗分布”或“預測分布”是對有關未知參或未來觀測所需了解的每項事物的概括。本書以一種強有力和貼切的方式說明了如何運用貝葉斯統(tǒng)計技術，引導讀者從具體數(shù)據(jù)中推測有關科學、醫(yī)療與社會問題的結論。本書解釋了貝葉斯方法論所需的一些細微假設，并展示了如何運用這些假設去獲取準確結論。本書所介紹的各種方法對計算機模擬的頻度特性方面也非常適用。本書生動地概述了有關費希爾方法（頻度方法），同時全面強調(diào)了似然性，適合作為主流統(tǒng)計學的教程。本書講述了效用理論的進展以及時間序列和預測，從而也適合計量經(jīng)濟學的學生閱讀。另外，本書還包括線性模型、范疇數(shù)據(jù)分析、生存競爭分析、隨機效應模型和非線性平滑等內(nèi)容。本書提供了許多運行實例、自學練習和實際應用，可作為高年級本科生和研究生的教材，同時也可供其他交叉學科的研究人員閱讀。
作者簡介
Thomas Leonard 于1973年在倫敦大學獲得統(tǒng)計學博士學位。他曾在沃里克大學工作過，于1995年擔任愛丁堡大學統(tǒng)計學系主席，還曾做過威斯康星-麥迪遜大學統(tǒng)計學教授。20世紀80年代，他最早將拉普斯算子引入到貝葉斯方法中。他發(fā)表了多篇有關統(tǒng)計學應用方面的論文，并作為統(tǒng)計學專家參與過多個美國法律訴訟案件。John S.J.Hsu 加州大學圣芭芭拉分校統(tǒng)計學與應用概率論副教授、統(tǒng)計實驗室主任，擅長研究應用問題，還建立了貝葉斯理論研究計劃。由于在log-線性模型分析方面的貢獻，他獲得了愛丁堡大學的名譽職位。在Thomas Leonard和Kam-Wah Tsui的指導下，他于1990年在威斯康星-麥迪遜大學獲得統(tǒng)計學博士學位。
圖書目錄
1 Introductory Statistical Concepts 1.0 Preliminaries and Overview 1.1 Sampling Models and Likelihoods 1.2 Practical Examples 1.3 Large Sample Properties of Likelihood Procedures 1.4 Practical Examples 1.5 Some Further Properties of Likelihood 1.6 Practical Examples 1.7 The Midcontinental Rift 1.8 A Model for Genetic Traits in Dairy Science 1.9 Least Squares Regression with Serially Correlated Errors 1.10 Annual World Crude Oil Production (1880-1972)2 The Discrete Version of Bayes' Theorem 2.0 .preliminaries and Overview 2.1 Bayes' Theorem 2.2 Estimating a Discrete-Valued Parameter 2.3 Applications to Model Selection 2.4 Practical Examples 2.5 Logistic Discrimination and the Construction of Neural Nets 2.6 Anderson's Prediction of Psychotic Patients 2.7 The Ontario Fetal Metabolic Acidosis Study 2.8 Practical Guidelines 3 Models with a Single Unknown Parameter 3.0 Preliminaries and Overview 3.1 The Bayesian Paradigm 3.2 Posterior and Predictive Inferences 3.3 Practical Examples 3.4 Inferences for a Normal Mean with Known Variance 3.5 Practical Examples 3.6 Vague Prior Information 3.7 Practical Examples 3.8 Bayes Estimators and Decision Rules and Their Frequency Properties 3.9 Practical Examples 3.10 Symmetric Loss Functions 3.11 Practical Example: Mixtures of Normal Distributions 4 The Expected Utility Hypothesis 4.0 Preliminaries and Overview 4.1 Classical Theory 4.2 The Savage Axioms 4.3 Modifications to the Expected Utility Hypothesis 4.4 The Experimental Measurement of 6-Adjusted Utility 4.5 The Risk-Aversion Paradox 4.6 The Ellsberg Paradox 4.7 A Practical Case Study 5 Models with Several Unknown Parameters 5.0 Preliminaries and Overview 5.1 Bayesian Marginalization 5.2 Further Methods and Practical Examples 5.3 The Kalman Filter 5.4 An On-Line Analysis of Chemical Process Readings 5.5 An Industrial Control Chart 5.6 Forecasting Geographical Proportions for World Sales of Fibers 5.7 Bayesian Forecasting in Economics 6 Prior Structures, Posterior Smoothing, and Bayes-Stein Estimation 6.0 Preliminaries and Overview 6.1 Multivariate Normal Priors for the Transformed Parameters 6.2 Posterior Mode Vectors and Laplacian Approximations 6.3 Prior Structures, and Modeling for Nonrandomized Data 6.4 Monte Carlo Methods and Importance Sampling 6.5 Further Special Cases and Practical Examples 6.6 Markov Chain Monte Carlo (MCMC) Methods:The Gibbs Sampler 6.7 Modeling Sampling Distributions, Using MCMC 6.8 Equally Weighted Mixtures and Survivor Functions 6.9 A Hierarchical Bayes Analysis References Author Index Subject Index
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