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Statistical Matching

A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches

  • Susanne Rässler

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

Table of contents

  1. Front Matter
    Pages N2-xviii
  2. Susanne Rässler
    Pages 1-14
  3. Susanne Rässler
    Pages 15-43
  4. Susanne Rässler
    Pages 44-70
  5. Susanne Rässler
    Pages 71-127
  6. Susanne Rässler
    Pages 128-199
  7. Susanne Rässler
    Pages 200-207
  8. Back Matter
    Pages 208-241

About this book

Introduction

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.

Keywords

Area Erlang Fusion Outlook Simula data analysis database distribution evaluation history of mathematics information production set statistics story

Authors and affiliations

  • Susanne Rässler
    • 1
  1. 1.Institute of Statistic and EconometricsUniversity of Erlangen-NürnbergNürnbergGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-0053-3
  • Copyright Information Springer-Verlag New York, Inc. 2002
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-95516-2
  • Online ISBN 978-1-4613-0053-3
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site
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