統(tǒng)計(jì)模型

內(nèi)容概要

本書是一本優(yōu)秀的統(tǒng)計(jì)模型教材，著重講解線性模型的應(yīng)用問題，包括廣義最小二乘和兩步最小二乘模型，以及二分變量的probit及l(fā)ogit模型的應(yīng)用，還包括關(guān)于研究設(shè)計(jì)、二分變量回歸及矩陣代數(shù)的背景知識(shí)。     這還是一本鼓舞人心的而又易讀的書，無論是老師還是學(xué)生都會(huì)從中受益。

作者簡介

（美）弗里德曼,是加州大學(xué)伯克利分校的統(tǒng)計(jì)學(xué)教授、杰出的數(shù)理統(tǒng)計(jì)學(xué)家。其研究范圍包括鞅不等式分析、Markov過程、抽樣、自助法等。他是美國科學(xué)學(xué)院士。在2003年。美國科學(xué)院授予他John J.Carty科學(xué)進(jìn)步獎(jiǎng)，以表彰他對統(tǒng)計(jì)理論和實(shí)踐做出的貢獻(xiàn)。

書籍目錄

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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