Sample Survey Calibration: An Information theoretic perspective

dc.creatorWittenberg, Martin
dc.date2012-12-03T12:05:03Z
dc.date2012-12-03T12:05:03Z
dc.date2009-10
dc.date.accessioned2015-05-28T10:04:59Z
dc.date.available2015-05-28T10:04:59Z
dc.date.issued2015-05-28
dc.descriptionWe show that the pseudo empirical maximum likelihood estimator can be recast as a calibration estimator. The process of estimating the probabilities pk of the distribution function can be done also in a maximum entropy framework. We suggest that a minimum cross-entropy estimator has attractive theoretical properties. A Monte Carlo simulation suggests that this estimator outperforms the PEMLE and the Horvitz-Thompson estimator. This is a joint SALDRU/DataFirst Working Paper as part of the Mellon Data Quality Project. For more information about the project visit www.datafirst.uct.ac.za.
dc.identifierhttp://hdl.handle.net/11090/14
dc.identifier.ris TY - Report DA - 2015-05-28 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Sample Survey Calibration: An Information theoretic perspective TI - Sample Survey Calibration: An Information theoretic perspective UR - http://hdl.handle.net/11090/14 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11090/14
dc.language.isoeng
dc.publisherSouthern Africa Labour and Development Research Unit
dc.publisher.departmentSALDRUen_ZA
dc.publisher.facultyFaculty of Commerceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.titleSample Survey Calibration: An Information theoretic perspective
dc.typeReport
uct.type.publicationResearchen_ZA
uct.type.resourceSALDRU Reporten_ZA
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