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Real donor imputation pools
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2012 (English)In: Proceedings of the Workshop of the Baltic-Nordic-Ukrainian network on survey statistics, 2012 / [ed] Mārtiņš Liberts, Valmiera, 2012, 162-168 p.Conference paper, Published paper (Other academic)
Abstract [en]

Real donor matching is associated with hot deck imputation. Aux-iliary variables are used to match donee units with missing values to aset of donor units with observed values, and the donee missing valuesare ‘replaced’ by copies of the donor values, as to create completelyfilled in datasets. The matching of donees and donors is complicatedby the fact that the observed sample survey data is often both sparseand bounded. The important choice of how many possible donors tochoose from involves a trade-off between bias and variance. We trans-fer concepts from kernel estimators to real donor imputation. In asimulation study we show how bias, variance and the estimated vari-ance of a population behaves, focusing on the size of donor pools.

Place, publisher, year, edition, pages
Valmiera, 2012. 162-168 p.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:su:diva-89431OAI: oai:DiVA.org:su-89431DiVA: diva2:617937
Conference
Workshop of the Baltic-Nordic-Ukrainian network on survey statistics, 2012.
Available from: 2013-04-25 Created: 2013-04-25 Last updated: 2013-04-25Bibliographically approved
In thesis
1. Multiple Kernel Imputation: A Locally Balanced Real Donor Method
Open this publication in new window or tab >>Multiple Kernel Imputation: A Locally Balanced Real Donor Method
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

We present an algorithm for imputation of incomplete datasets based on Bayesian exchangeability through Pólya sampling. Each (donee) unit with a missing value is imputed multiple times by observed (real) values on units from a donor pool. The donor pools are constructed using auxiliary variables. Several features from kernel estimation are used to counteract unbalances that are due to sparse and bounded data. Three balancing features can be used with only one single continuous auxiliary variable, but an additional fourth feature need, multiple continuous auxiliary variables. They mainly contribute by reducing nonresponse bias. We examine how the donor pool size should be determined, that is the number of potential donors within the pool. External information is shown to be easily incorporated in the imputation algorithm. Our simulation studies show that with a study variable which can be seen as a function of one or two continuous auxiliaries plus residual noise, the method performs as well or almost as well as competing methods when the function is linear, but usually much better when the function is nonlinear.

Place, publisher, year, edition, pages
Stockholm: Department of Statistics, Stockholm University, 2013. 40 p.
Keyword
Bayesian Bootstrap, Boundary Effects, External Information, Kernel estimation features, Local Balancing, Pólya Sampling
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:su:diva-89435 (URN)978-91-7447-699-6 (ISBN)
Public defence
2013-05-28, hörsal 4, hus B, Universitetsvägen 10 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: In press. Paper 3: Submitted. Paper 4: Submitted.

Available from: 2013-05-06 Created: 2013-04-25 Last updated: 2014-06-02Bibliographically approved

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