Obtaining cancer risk factor prevalence estimates in small areas: combining data from two surveys
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Abstract
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DOI: 10.1111/j.1467-9876.2005.05459.x
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Cited by:
- Anika Rasner & Joachim R. Frick & Markus M. Grabka, 2013. "Statistical Matching of Administrative and Survey Data," Sociological Methods & Research, , vol. 42(2), pages 192-224, May.
- Giancarlo Manzi & David J. Spiegelhalter & Rebecca M. Turner & Julian Flowers & Simon G. Thompson, 2011. "Modelling bias in combining small area prevalence estimates from multiple surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 31-50, January.
- Jae Kwang Kim & Zhonglei Wang & Zhengyuan Zhu & Nathan B. Cruze, 2018. "Combining Survey and Non-survey Data for Improved Sub-area Prediction Using a Multi-level Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 175-189, June.
- Marissa B. Reitsma & Sherri Rose & Alex Reinhart & Jeremy D. Goldhaber-Fiebert & Joshua A. Salomon, 2024. "Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey," Medical Decision Making, , vol. 44(2), pages 175-188, February.
- Takis Merkouris, 2010. "Combining information from multiple surveys by using regression for efficient small domain estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 27-48, January.
- Rasner, Anika & Frick, Joachim R. & Grabka, Markus M., 2013. "Statistical Matching of Administrative and Survey Data: An Application to Wealth Inequality Analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 42(2), pages 192-224.
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