{"product_id":"statistical-matching-9780470023532","title":"Statistical Matching","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThere is more statistical data produced in today's modern society than ever before. This data is analysed and cross-referenced for innumerable reasons. However, many data sets have no shared element and are harder to combine and therefore obtain any meaningful inference from.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Those interested in statistical matching will find this book very useful.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, August 2007)  \u003cp\u003e\"My compliments to the authors for making these (seemingly) arcane ideas available to a whole new generation of statisticians and economists.\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, September 2007)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 The Statistical Matching Problem.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 The Statistical Framework.\u003c\/p\u003e \u003cp\u003e1.3 The Missing Data Mechanism in the Statistical Matching Problem.\u003c\/p\u003e \u003cp\u003e1.4 Accuracy of a Statistical Matching Procedure.\u003c\/p\u003e \u003cp\u003e1.4.1 Model assumptions.\u003c\/p\u003e \u003cp\u003e1.4.2 Accuracy of the estimator.\u003c\/p\u003e \u003cp\u003e1.4.3 Representativeness of the synthetic file.\u003c\/p\u003e \u003cp\u003e1.4.4 Accuracy of estimators applied on the synthetic data set.\u003c\/p\u003e \u003cp\u003e1.5 Outline of the Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Conditional Independence Assumption.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The Macro Approach in a Parametric Setting.\u003c\/p\u003e \u003cp\u003e2.1.1 Univariate normal distributions case.\u003c\/p\u003e \u003cp\u003e2.1.2 The multinormal case.\u003c\/p\u003e \u003cp\u003e2.1.3 The multinomial case.\u003c\/p\u003e \u003cp\u003e2.2 The Micro (Predictive) Approach in the Parametric Framework.\u003c\/p\u003e \u003cp\u003e2.2.1 Conditional mean matching.\u003c\/p\u003e \u003cp\u003e2.2.2 Draws based on conditional predictive distributions.\u003c\/p\u003e \u003cp\u003e2.2.3 Representativeness of the predicted files.\u003c\/p\u003e \u003cp\u003e2.3 Nonparametric Macro Methods.\u003c\/p\u003e \u003cp\u003e2.4 The Nonparametric Micro Approach.\u003c\/p\u003e \u003cp\u003e2.4.1 Random hot deck.\u003c\/p\u003e \u003cp\u003e2.4.2 Rank hot deck.\u003c\/p\u003e \u003cp\u003e2.4.3 Distance hot deck.\u003c\/p\u003e \u003cp\u003e2.4.4 The matching noise.\u003c\/p\u003e \u003cp\u003e2.5 Mixed Methods.\u003c\/p\u003e \u003cp\u003e2.5.1 Continuous variables.\u003c\/p\u003e \u003cp\u003e2.5.2 Categorical variables.\u003c\/p\u003e \u003cp\u003e2.6 Comparison of Some Statistical Matching Procedures under the CIA.\u003c\/p\u003e \u003cp\u003e2.7 The Bayesian Approach.\u003c\/p\u003e \u003cp\u003e2.8 Other IdentifiableModels.\u003c\/p\u003e \u003cp\u003e2.8.1 The pairwise independence assumption.\u003c\/p\u003e \u003cp\u003e2.8.2 Finite mixture models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Auxiliary Information.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Different Kinds of Auxiliary Information.\u003c\/p\u003e \u003cp\u003e3.2 Parametric Macro Methods.\u003c\/p\u003e \u003cp\u003e3.2.1 The use of a complete third file.\u003c\/p\u003e \u003cp\u003e3.2.2 The use of an incomplete third file.\u003c\/p\u003e \u003cp\u003e3.2.3 The use of information on inestimable parameters.\u003c\/p\u003e \u003cp\u003e3.2.4 The multinormal case.\u003c\/p\u003e \u003cp\u003e3.2.5 Comparison of different regression parameter estimators through simulation.\u003c\/p\u003e \u003cp\u003e3.2.6 The multinomial case.\u003c\/p\u003e \u003cp\u003e3.3 Parametric Predictive Approaches.\u003c\/p\u003e \u003cp\u003e3.4 Nonparametric Macro Methods.\u003c\/p\u003e \u003cp\u003e3.5 The Nonparametric Micro Approach with Auxiliary Information.\u003c\/p\u003e \u003cp\u003e3.6 Mixed Methods.\u003c\/p\u003e \u003cp\u003e3.6.1 Continuous variables.\u003c\/p\u003e \u003cp\u003e3.6.2 Comparison between some mixed methods.\u003c\/p\u003e \u003cp\u003e3.6.3 Categorical variables.\u003c\/p\u003e \u003cp\u003e3.7 Categorical Constrained Techniques.\u003c\/p\u003e \u003cp\u003e3.7.1 Auxiliary micro information and categorical constraints.\u003c\/p\u003e \u003cp\u003e3.7.2 Auxiliary information in the form of categorical constraints.\u003c\/p\u003e \u003cp\u003e3.8 The Bayesian Approach.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Uncertainty in Statistical Matching.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 A Formal Definition of Uncertainty.\u003c\/p\u003e \u003cp\u003e4.3 Measures of Uncertainty.\u003c\/p\u003e \u003cp\u003e4.3.1 Uncertainty in the normal case.\u003c\/p\u003e \u003cp\u003e4.3.2 Uncertainty in the multinomial case.\u003c\/p\u003e \u003cp\u003e4.4 Estimation of Uncertainty.\u003c\/p\u003e \u003cp\u003e4.4.1 Maximum likelihood estimation of uncertainty in the multinormal case.\u003c\/p\u003e \u003cp\u003e4.4.2 Maximum likelihood estimation of uncertainty in the multinomial case.\u003c\/p\u003e \u003cp\u003e4.5 Reduction of Uncertainty: Use of Parameter Constraints.\u003c\/p\u003e \u003cp\u003e4.5.1 The multinomial case.\u003c\/p\u003e \u003cp\u003e4.6 Further Aspects of Maximum Likelihood Estimation of Uncertainty.\u003c\/p\u003e \u003cp\u003e4.7 An Example with Real Data.\u003c\/p\u003e \u003cp\u003e4.8 Other Approaches to the Assessment of Uncertainty.\u003c\/p\u003e \u003cp\u003e4.8.1 The consistent approach.\u003c\/p\u003e \u003cp\u003e4.8.2 The multiple imputation approach.\u003c\/p\u003e \u003cp\u003e4.8.3 The de Finetti coherence approach.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Statistical Matching and Finite Populations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Matching Two Archives.\u003c\/p\u003e \u003cp\u003e5.1.1 Definition of the CIA.\u003c\/p\u003e \u003cp\u003e5.2 Statistical Matching and Sampling from a Finite Population.\u003c\/p\u003e \u003cp\u003e5.3 Parametric Methods under the CIA.\u003c\/p\u003e \u003cp\u003e5.3.1 The macro approach when the CIA holds.\u003c\/p\u003e \u003cp\u003e5.3.2 The predictive approach.\u003c\/p\u003e \u003cp\u003e5.4 Parametric Methods when Auxiliary Information is Available.\u003c\/p\u003e \u003cp\u003e5.4.1 The macro approach.\u003c\/p\u003e \u003cp\u003e5.4.2 The predictive approach.\u003c\/p\u003e \u003cp\u003e5.5 File Concatenation.\u003c\/p\u003e \u003cp\u003e5.6 Nonparametric Methods.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Issues in Preparing for Statistical Matching.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Reconciliation of Concepts and Definitions of Two Sources.\u003c\/p\u003e \u003cp\u003e6.1.1 Reconciliation of biased sources.\u003c\/p\u003e \u003cp\u003e6.1.2 Reconciliation of inconsistent definitions.\u003c\/p\u003e \u003cp\u003e6.2 How to Choose the Matching Variables.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Applications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Case Study: The Social Accounting Matrix.\u003c\/p\u003e \u003cp\u003e7.2.1 Harmonization step.\u003c\/p\u003e \u003cp\u003e7.2.2 Modelling the social accounting matrix.\u003c\/p\u003e \u003cp\u003e7.2.3 Choosing the matching variables.\u003c\/p\u003e \u003cp\u003e7.2.4 The SAM under the CIA.\u003c\/p\u003e \u003cp\u003e7.2.5 The SAM and auxiliary information.\u003c\/p\u003e \u003cp\u003e7.2.6 Assessment of uncertainty for the SAM.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Statistical Methods for Partially Observed Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Maximum Likelihood Estimation with Missing Data.\u003c\/p\u003e \u003cp\u003eA.1.1 Missing data mechanisms.\u003c\/p\u003e \u003cp\u003eA.1.2 Maximum likelihood and ignorable nonresponse.\u003c\/p\u003e \u003cp\u003eA.2 Bayesian Inference withMissing Data.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Loglinear Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Maximum Likelihood Estimation of the Parameters.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC Distance Functions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD Finite Population Sampling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eE R Code.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eE.1 The R Environment.\u003c\/p\u003e \u003cp\u003eE.2 R Code for Nonparametric Methods.\u003c\/p\u003e \u003cp\u003eE.3 R Code for Parametric and Mixed Methods.\u003c\/p\u003e \u003cp\u003eE.4 R Code for the Study of Uncertainty.\u003c\/p\u003e \u003cp\u003eE.5 Other R Functions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default 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