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  • 1
    UID:
    almahu_9947362965402882
    Format: XVIII, 264 p. , online resource.
    ISBN: 9781461300533
    Series Statement: Lecture Notes in Statistics, 168
    Content: Data fusion or statistical file matching techniques merge data sets from different survey samples to solve the problem that exists when no single file contains all the variables of interest. Media agencies are merging television and purchasing data, statistical offices match tax information with income surveys. Many traditional applications are known but information about these procedures is often difficult to achieve. The author proposes the use of multiple imputation (MI) techniques using informative prior distributions to overcome the conditional independence assumption. By means of MI sensitivity of the unconditional association of the variables not jointy observed can be displayed. An application of the alternative approaches with real world data concludes the book.
    Note: 1.1 Statistical Matching — Problems and Perspectives -- 1.2 Record Linkage Versu s Statistical Matching -- 1.3 Statistical Matching as Nonresponse Phenomenon -- 1.4 Identification Problems Inherent in Statistical Matching -- 1.5 Outline of th e Book -- 1.6 Bibliographic and Software Notes -- Frequentist Theory of Statistical Matching -- 2.1 Introduction and Chapters Outline -- 2.2 The Matching Process -- 2.3 Properties of the Matching Process -- 2.4 Matching by Propensity Scores -- 2.5 Obj ectives of Statisti cal Matching -- 2.6 Some Illustrations -- 2.7 Concluding Remarks -- Practical Applications of Statistical Matching -- 3.1 Introduction and Chapters Outline -- 3.2 History of Statistical Matching Techniques -- 3.3 Overview of Traditional Approaches -- Alternative Approaches to Statistical Matching -- 4.1 Introduction and Chapters Outline -- 4.2 Some Basic Notation -- 4.3 Multiple Imputation Inference -- 4.4 Regression Imputation with Random Residuals -- 4.5 Noniterative Multivariate Imputation Procedure -- 4.6 Data Augmentation -- 4.7 Iterative Univariate Imputations by Chained Equ ations -- 4.8 Simulation Study — Multivariate Normal Data -- 4.9 Concluding Remarks -- Empirical Evaluation of Alternative Approaches -- 5.1 Introduction and Chapters Outline -- 5.2 Simulation Study Using Survey Data -- 5.3 Simulation Study Using Generated Data -- 5.4 Design of the Evaluation Study -- 5.5 Results Due to Alternative Approaches -- 5.6 Concluding Remarks -- Synopsis and Outlook -- 6.1 Synopsis -- 6.2 Outlook -- Some Technicalities -- Multivariate Normal Model Completely Observed -- Normally Distributed Data Not Jointly Observed -- Basic S-PLUS Routines -- EVALprio -- EVALd -- NIBAS -- Tables -- References.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9780387955162
    Language: English
    URL: Volltext  (lizenzpflichtig)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    gbv_34806795X
    Format: XVIII, 238 S , graph. Darst , 24 cm
    ISBN: 9780387955162 , 038795516X
    Series Statement: Lecture notes in statistics 168
    Note: Includes bibliographical references and index , Includes bibliographical references (p. [226] - 234) and index
    Additional Edition: Erscheint auch als Online-Ausgabe Rässler, Susanne, 1962 - 2018 Statistical Matching New York, NY : Springer, 2002 ISBN 9781461300533
    Language: English
    Subjects: Mathematics
    RVK:
    Keywords: Statistik ; Matching
    Author information: Rässler, Susanne 1962-2018
    Library Location Call Number Volume/Issue/Year Availability
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