Multiple Kernel Imputation: A Locally Balanced Real Donor Method
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
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.
Bayesian Bootstrap, Boundary Effects, External Information, Kernel estimation features, Local Balancing, Pólya Sampling
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:su:diva-89435ISBN: 978-91-7447-699-6OAI: oai:DiVA.org:su-89435DiVA: diva2:617951
2013-05-28, hörsal 4, hus B, Universitetsvägen 10 B, Stockholm, 10:00 (English)
Rässler, Susanne, Professor
Thorburn, Daniel, Professor emeritus
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: In press.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.2013-05-062013-04-252014-06-02Bibliographically approved
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