Evaluation of statistical matching and selected SAE methods [electronic resource] : using micro census and EU-SILC data / Verena Puchner.
2015
HC79.P6 P83 2015eb
Formats
| Format | |
|---|---|
| BibTeX | |
| MARCXML | |
| TextMARC | |
| MARC | |
| DublinCore | |
| EndNote | |
| NLM | |
| RefWorks | |
| RIS |
Cite
Citation
Linked e-resources
Linked Resource
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Evaluation of statistical matching and selected SAE methods [electronic resource] : using micro census and EU-SILC data / Verena Puchner.
Author
ISBN
9783658082246 electronic book
3658082240 electronic book
9783658082239
3658082240 electronic book
9783658082239
Published
Wiesbaden [Germany] : Springer Spektrum, [2015]
Copyright
©2015
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-658-08224-6 doi
Call Number
HC79.P6 P83 2015eb
Dewey Decimal Classification
339.4/60727
Summary
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. Contents Regression Models Including Selected Small Area Methods Statistical Matching Application to Poverty Estimation Using EU-SILC and Micro Census Data Bootstrap Methods Target Groups Researchers, students, and practitioners in the fields of statistics, official statistics, and survey statistics 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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Series
BestMasters.
Available in Other Form
Print version: 9783658082239
Linked Resources
Record Appears in
Table of Contents
Regression Models Including Selected Small Area Methods
Statistical Matching
Application to Poverty Estimation Using EU-SILC and Micro Census Data
Bootstrap Methods.
Statistical Matching
Application to Poverty Estimation Using EU-SILC and Micro Census Data
Bootstrap Methods.