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1639548 D'Orazio, Marcello; Di Zio, Marco; Scanu, Mauro:
Statistical Matching
 Theory and Practice
 Preis:   € 83,90

Reihe: Wiley Series in Survey Methodology, Einband: Gb
Auflage: 1. Auflage
Verlag: John Wiley & Sons
Erscheinungsdatum: 03/2006
Seiten: 268 S.

ISBN-10: 0-470-02353-8   
ISBN-13: 978-0-470-02353-2

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Beschreibung
There 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. Statistical matching allows just that; it is the art of combining information from different sources (particularly sample surveys) that contain no common unit. In response to modern influxes of data, it is an area of rapidly growing interest and complexity. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications.
* Presents a unified framework for both theoretical and practical aspects of statistical matching.
* Provides a detailed description covering all the steps needed to perform statistical matching.
* Contains a critical overview of the available statistical matching methods.
* Discusses all the major issues in detail, such as the Conditional Independence Assumption and the assessment of uncertainty.
* Includes numerous examples and applications, enabling the reader to apply the methods in their own work.
* Features an appendix detailing algorithms written in the R language.
Statistical Matching: Theory and Practice presents a comprehensive exploration of an increasingly important area. Ideal for researchers in national statistics institutes and applied statisticians, it will also prove to be an invaluable text for scientists and researchers from all disciplines engaged in the multivariate analysis of data collected from different sources.
Inhalt
Preface.
1 The Statistical Matching Problem.
1.1 Introduction.
1.2 The Statistical Framework.
1.3 The Missing Data Mechanism in the Statistical Matching Problem.
1.4 Accuracy of a Statistical Matching Procedure.
1.5 Outline of the Book.
2 The Conditional Independence Assumption.
2.1 The Macro Approach in a Parametric Setting.
2.2 The Micro (Predictive) Approach in the Parametric Framework.
2.3 Nonparametric Macro Methods.
2.4 The Nonparametric Micro Approach.
2.5 Mixed Methods.
2.6 Comparison of Some Statistical Matching Procedures under the CIA.
2.7 The Bayesian Approach.
2.8 Other IdentifiableModels.
3 Auxiliary Information.
3.1 Different Kinds of Auxiliary Information.
3.2 Parametric Macro Methods.
3.3 Parametric Predictive Approaches.
3.4 Nonparametric Macro Methods.
3.5 The Nonparametric Micro Approach with Auxiliary Information.
3.6 Mixed Methods.
3.7 Categorical Constrained Techniques.
3.8 The Bayesian Approach.
4 Uncertainty in Statistical Matching.
4.1 Introduction.
4.2 A Formal Definition of Uncertainty.
4.3 Measures of Uncertainty.
4.4 Estimation of Uncertainty.
4.5 Reduction of Uncertainty: Use of Parameter Constraints.
4.6 Further Aspects of Maximum Likelihood Estimation of Uncertainty.
4.7 An Example with Real Data.
4.8 Other Approaches to the Assessment of Uncertainty.
5 Statistical Matching and Finite Populations.
5.1 Matching Two Archives.
5.2 Statistical Matching and Sampling from a Finite Population.
5.3 Parametric Methods under the CIA.
5.4 Parametric Methods when Auxiliary Information is Available.
5.5 File Concatenation.
5.6 Nonparametric Methods.
6 Issues in Preparing for Statistical Matching.
6.1 Reconciliation of Concepts and Definitions of Two Sources.
6.2 How to Choose the Matching Variables.
7 Applications.
7.1 Introduction.
7.2 Case Study: The Social Accounting Matrix.
A Statistical Methods for Partially Observed Data.
A.1 Maximum Likelihood Estimation with Missing Data.
A.2 Bayesian Inference withMissing Data.
B Loglinear Models.
B.1 Maximum Likelihood Estimation of the Parameters.
C Distance Functions.
D Finite Population Sampling.
E R Code.
E.1 The R Environment.
E.2 R Code for Nonparametric Methods.
E.3 R Code for Parametric and Mixed Methods.
E.4 R Code for the Study of Uncertainty.
E.5 Other R Functions.
References.
Index.
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