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Statistical Matching

A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches

  • Book
  • © 2002

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Part of the book series: Lecture Notes in Statistics (LNS, volume 168)

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About this book

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.

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Keywords

Table of contents (6 chapters)

Reviews

"Statistical matching is one of the methods for the imputation of missing results from survey sampling…This book is totally focused on methods, applications, and results. It devotes little space to the presentation and derivation of various theorems." Technometrics, May 2004

Authors and Affiliations

  • Institute of Statistic and Econometrics, University of Erlangen-Nürnberg, Nürnberg, Germany

    Susanne Rässler

Bibliographic Information

  • Book Title: Statistical Matching

  • Book Subtitle: A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches

  • Authors: Susanne Rässler

  • Series Title: Lecture Notes in Statistics

  • DOI: https://doi.org/10.1007/978-1-4613-0053-3

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 2002

  • Softcover ISBN: 978-0-387-95516-2Published: 21 August 2002

  • eBook ISBN: 978-1-4613-0053-3Published: 06 December 2012

  • Series ISSN: 0930-0325

  • Series E-ISSN: 2197-7186

  • Edition Number: 1

  • Number of Pages: XVIII, 264

  • Topics: Statistical Theory and Methods

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