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Real donor imputation pools
Statistiska institutionen, Stockholms universitet, Stockholm, Sverige.ORCID-id: 0000-0002-3888-4695
2012 (engelsk)Inngår i: Proceedings of the Workshop of the Baltic-Nordic-Ukrainian network on survey statistics, 2012 / [ed] Mārtiņš Liberts, Valmiera: University of Latvia , 2012, s. 162-168Konferansepaper, Publicerat paper (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Valmiera: University of Latvia , 2012. s. 162-168
HSV kategori
Identifikatorer
URN: urn:nbn:se:oru:diva-43205ISBN: 978-9984-45-557-0 (tryckt)OAI: oai:DiVA.org:oru-43205DiVA, id: diva2:791918
Konferanse
Workshop of the Baltic-Nordic-Ukrainian network on survey statistics, 2012.
Tilgjengelig fra: 2013-04-25 Laget: 2015-03-02 Sist oppdatert: 2019-04-11bibliografisk kontrollert
Inngår i avhandling
1. Multiple Kernel Imputation: A Locally Balanced Real Donor Method
Åpne denne publikasjonen i ny fane eller vindu >>Multiple Kernel Imputation: A Locally Balanced Real Donor Method
2013 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: Department of Statistics, Stockholm University, 2013. s. 40
Emneord
Bayesian Bootstrap, Boundary Effects, External Information, Kernel estimation features, Local Balancing, Pólya Sampling
HSV kategori
Forskningsprogram
statistik
Identifikatorer
urn:nbn:se:oru:diva-43203 (URN)978-91-7447-699-6 (ISBN)
Disputas
2013-05-28, hörsal 4, hus B, Universitetsvägen 10 B, 10:00 (engelsk)
Opponent
Veileder
Merknad

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.

Tilgjengelig fra: 2015-03-20 Laget: 2015-03-02 Sist oppdatert: 2018-05-26bibliografisk kontrollert

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