Sample Survey Calibration: An Informationtheoretic perspective
AbstractWe 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.
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Bibliographic InfoPaper provided by Southern Africa Labour and Development Research Unit, University of Cape Town in its series SALDRU Working Papers with number 41.
Date of creation: Oct 2009
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sample weights; calibration; pseudo-empirical maximum likelihood estimation; cross entropy;
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- Joachim Merz & Henning Stolze, 2008.
"Representative time use data and new harmonised calibration of the American Heritage Time Use Data (AHTUD) 1965-1999,"
electronic International Journal of Time Use Research,
Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)) and The International Association for Time Use Research (IATUR), vol. 5(1), pages 90-126, November.
- Merz, Joachim & Stolze, Henning, 2008. "Representative time use data and new harmonised calibration of the American Heritage Time Use Data (AHTUD) 1965-1999," MPRA Paper 11651, University Library of Munich, Germany.
- Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
- Nicola Branson & Martin Wittenberg, 2011. "Re-weighting South African National Household Survey Data to create a consistent series over time: A cross entropy estimation approach," SALDRU Working Papers 54, Southern Africa Labour and Development Research Unit, University of Cape Town.
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