出版时间:2010-9 出版社:机械工业出版社 作者:弗里德曼 页数:442
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内容概要
本书是一本优秀的统计模型教材,着重讲解线性模型的应用问题,包括广义最小二乘和两步最小二乘模型,以及二分变量的probit及logit模型的应用,还包括关于研究设计、二分变量回归及矩阵代数的背景知识。 这还是一本鼓舞人心的而又易读的书,无论是老师还是学生都会从中受益。
作者简介
(美)弗里德曼,是加州大学伯克利分校的统计学教授、杰出的数理统计学家。其研究范围包括鞅不等式分析、Markov过程、抽样、自助法等。他是美国科学学院士。在2003年。美国科学院授予他John J.Carty科学进步奖,以表彰他对统计理论和实践做出的贡献。
书籍目录
Foreword to the ReVised EditionPreface1 Observational Studies and Experiments 1.1 Introduction 1.2 The HIP trial 1.3 Snow on cholera 1.4 Yule on the causes of poverty Exercise set A 1.5 End notes 2 The Regression Line 2.1 Introduction 2.2 The regression line 2.3 Hooke's law Exercise set A 2.4 Complexities 2.5 Simple vs multiple regression Exercise set B 2.6 End notes3 Matrix Algebra 3.1 Introduction Exercise set A 3.2 Determinants and inverses Exercise set B 3.3 Random vectors Exercise set C 3.4 Positive definite matrices Exercise set D 3.5 The normal distribution Exercise set E 3.6 If you want a book on matrix algebra4 Multiple Regression 4.1 Introduction Exercise set A 4.2 Standard errors Things we don't need Exercise set B 4.3 Explained variance in multiple regression Association or causation? Exercise set C 4.4 What happens to OLS if the assumptions break down? 4.5 Discussion questions 4.6 End notes5 Multiple Regression: Special Topics 5.1 Introduction 5.2 OLSisBLUE Exercise set A 5.3 Generalized least squares Exercise set B 5.4 Examples on GLS Exercise set C 5.5 What happens to GLS if the assumptions break down? 5.6 Normal theory Statistical significance Exercise set D 5.7 The F-test "The" F-test in applied work Exercise set E 5.8 Data snooping Exercise set F 5.9 Discussion questions 5.10 End notes6 Path Models 6.1 Stratification Exercise set A 6.2 Hooke's law revisited Exercise set B 6.3 Political repression during the McCarthy era Exercise set C 6.4 Inferring causation .by regression Exercise set D 6.5 Response schedules for path diagrams Selection vs intervention Structural equations and stable parameter:Ambiguity in notation Exercise set E 6.6 Dummy variables Types of variables 6.7 Discussion questions 6.8 End notes7 Maximum Likelihood 7.1 Introduction Exercise set A 7.2 Probit models Why not regression? The latent-variable formulation Exercise set B Identification vs estimation What if the Ui are N? Exercise set C 7.3 Logit models Exercise set D 7.4 The effect of Catholic schools Latent variables Response schedules The second equation Mechanics: bivariate probit Why a model rather than a cross-lab? Interactions More on table 3 in Evans and Schwab More on the second equation Exercise set E 7.5 Discussion questions 7.6 End notes8 The Bootstrap 8.1 Introduction Exercise set A 8.2 Bootstrapping a model for energy demand Exercise set B 8.3 End notes9 Simultaneous Equations 9.1 Introduction Exercise set A 9.2 Instrumental variables Exercise set B 9.3 Estimating the butter model Exercise set C 9.4 What are the two stages? Invariance assumptions 9.5 A social-science example: education and fertility More on Rindfuss et al 9.6 Covariates 9.7 Linear probability models The assumptions The questions Exercise set D 9.8 More on IVLS Some technical issues Exercise set E Simulations to illustrate IVLS 9.9 Discussion questions 9.10 End notes10 Issues in Statistical Modeling 10.1 Introduction The bootstrap The role of asymptotics Philosophers' stones The modelers' response 10.2 Critical literature 10.3 Response schedules 10.4 Evaluating the models in chapters 7-9 10.5 Summing upReferencesAnswers to ExercisesThe Computer LabsAppendix: Sample MATLAB CodeReprints Gibson on McCarthy Evans and Schwab on Catholic Schools Rindfuss et al on Education and Fertility Schneider et al on Social CapitalIndex
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