Skip to main content
SpringerLink
Log in

Evaluation of Statistical Matching and Selected SAE Methods

Using Micro Census and EU-SILC Data

  • Book
  • © 2015

Overview

Authors:
  1. Verena Puchner

    1. Tieto Austria GmbH, Vienna, Austria

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Study in the field of technical sciences
  • Includes supplementary material: sn.pub/extras

Part of the book series: BestMasters (BEST)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (5 chapters)

  1. Front Matter

    Pages I-XIII
    Download chapter PDF

  2. Introduction

    • Verena Puchner
    Pages 1-2

  3. Mathematical Background

    • Verena Puchner
    Pages 3-15

  4. Design of the Simulation Study

    • Verena Puchner
    Pages 17-22

  5. Application to Poverty Estimation Using EU-SILC and Micro Census Data

    • Verena Puchner
    Pages 23-62

  6. Conclusion

    • Verena Puchner
    Pages 63-64

  7. Back Matter

    Pages 65-101
    Download chapter PDF
Back to top

Keywords

About this book

Verena Puchner evaluates and compares statistical matching and selected SAE methods. Due to the fact that poverty estimation at regional level based on EU-SILC samples is not of adequate accuracy, the quality of the estimations should be improved by additionally incorporating micro census data. The aim is to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial "close-to-reality" population. Variables of interest are imputed into the micro census data sets with the help of the EU-SILC samples through regression models including selected unit-level small area methods and statistical matching methods. Poverty indicators are then estimated. The author evaluates and compares the bias and variance for the direct estimator and the various methods. The variance is desired to be reduced by the larger sample size of the micro census.

Authors and Affiliations

  • Tieto Austria GmbH, Vienna, Austria

    Verena Puchner

About the author

Verena Puchner obtained her master’s degree at Technical University of Vienna under the supervision of Priv.-Doz. Dipl.-Ing. Dr. techn. Matthias Templ. At present, she works as a data miner and consultant.

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation