References
- R. Arnab, Survey Sampling Theory and Applications, Academic Press, 2017.
- T. Banerjee, and A. Nayak, US county level analysis to determine if social distancing slowed the spread of COVID-19. Revista Panamericana de Salud Pública 44 (2020), pp. e90.
- Z. Berkowitz, X. Zhang, T.B. Richards, et al., Multilevel regression for small-area estimation of mammography Use in the United States, 2014. Cancer Epidem Biomar 28 (2019), pp. 32–40. doi:10.1158/1055-9965.EPI-18-0367.
- Z. Berkowitz, X. Zhang, T.B. Richards, et al., Multilevel small-area estimation of multiple cigarette smoking status categories using the 2012 behavioral risk factor surveillance system. Cancer Epidemiol. Biomarkers Prev. 25 (2016), pp. 1402–1410.
- J.G. Booth, and J.P. Hobert, Standard errors of prediction in generalized linear mixed models. J. Am. Stat. Assoc. 93 (1998), pp. 262–272.
- N.E. Breslow, and D.G. Clayton, Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88 (1993), pp. 9–25.
- R.J. Carroll, D. Ruppert, L.A. Stefanski, et al., Measurement Error in Nonlinear Models: A Modern Perspective, CRC press, 2006.
- N.J. Christian, I.D. Ha, and J.H. Jeong, Hierarchical likelihood inference on clustered competing risks data. Stat. Med. 35 (2016), pp. 251–267.
- M. Dahab, K. van Zandvoort, S. Flasche, et al., COVID-19 control in low-income settings and displaced populations: what can realistically be done? Confl. Health. 14 (2020), pp. 1–6.
- H. Dai, A Community Data-Driven Policy Analysis Framework to Promote Tobacco 21 in the United States. Funded by the Robert Wood Johnson Foundation https://wwwevidenceforactionorg/grant/community-data-driven-policy-analysis-framework-promote-tobacco-21-united-states 2019.
- Daily Travel during the COVID-19 Public Health Emergency, https://www.bts.gov/browse-statistical-products-and-data/trips-distance/explore-us-mobility-during-covid-19-pandemic (2020, accessed 08/15/2020).
- G.S. Datta, Model-based approach to small area estimation. Handbook of Statistics. Elsevier (2009), pp. 251–288.
- P. Dick, Modelling net undercoverage in the 1991 Canadian census. Surv. Methodol. 21 (1995), pp. 45–54.
- J.R. DiFranza, J.A. Savageau, and K.E. Fletcher, Enforcement of underage sales laws as a predictor of daily smoking among adolescents: a national study. BMC Public Health 9 (2009), pp. 107. DOI: 10.1186/1471-2458-9-107.
- E. Dong, H. Du, and L. Gardner, An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20 (2020), pp. 533–534.
- R.E. Fay III, and R.A. Herriot, Estimates of income for small places: an application of james-stein procedures to census data. J. Am. Stat. Assoc. 74 (1979), pp. 269–277.
- A.S. Gentzke, M. Creamer, K.A. Cullen, et al., Vital signs: tobacco product use among middle and high school students - United States, 2011–2018. MMWR Morb. Mortal. Wkly. Rep. 68 (2019), pp. 157–164. 2019/02/15. DOI: 10.15585/mmwr.mm6806e1.
- M. Ghosh, K. Natarajan, T. Stroud, et al., Generalized linear models for small-area estimation. J. Am. Stat. Assoc. 93 (1998), pp. 273–282.
- M. Ghosh, and J. Rao, Small area estimation: an appraisal. Stat. Sci. 9 (1994), pp. 55–76.
- I.D. Ha, and Y. Lee, Estimating frailty models via poisson hierarchical generalized linear models. J. Comput. Graph. Stat. 12 (2003), pp. 663–681.
- I.D. Ha, R. Sylvester, C. Legrand, et al., Frailty modelling for survival data from multi-centre clinical trials. Stat. Med. 30 (2011), pp. 2144–2159.
- Harmful Chemicals in Electronic Cigarettes, http://www.gaspforair.org/gasp/gedc/pdf/E-CigSmoke.pdf.
- J. Jeon, L. Hsu, and M. Gorfine, Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data. Biostatistics 13 (2012), pp. 384–397.
- J. Jiang, On maximum hierarchical likelihood estimators. Commun Stat-Theory Methods 28 (1999), pp. 1769–1775.
- J. Jiang, and P. Lahiri, Mixed model prediction and small area estimation. Test 15 (2006), pp. 1.
- Y. Lee, and J.A. Nelder, Hierarchical generalized linear models. J Roy Stat Soc B Met 58 (1996), pp. 619–656.
- Y. Lee, J.A. Nelder, and Y. Pawitan, Generalized Linear Models with Random Effects: Unified Analysis via H-Likelihood, CRC Press, 2018.
- Q. Liu, and D.A. Pierce, Heterogeneity in mantel-haenszel-type models. Biometrika 80 (1993), pp. 543–556.
- M. Martuzzi, and P. Elliott, Empirical Bayes estimation of small area prevalence of non-rare conditions. Stat. Med. 15 (1996), pp. 1867–1873.
- F. Mauro, V.J. Monleon, H. Temesgen, et al., Analysis of area level and unit level models for small area estimation in forest inventories assisted with LiDAR auxiliary information. PloS one 12 (2017), p. e0189401.
- P. McCullagh, Generalized Linear Models, Routledge, 2018.
- National Center for Chronic Disease Prevention and Health Promotion. Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon General Atlanta,4, http://www.ncbi.nlm.nih.gov/books/NBK99236/. 2012.
- A. Rahman, and A. Harding, Small Area Estimation and Microsimulation Modeling, Chapman and Hall/CRC, 2016.
- J.N. Rao, and I. Molina, Small Area Estimation, John Wiley & Sons, 2015.
- N.A. Rigotti, and S. Kalkhoran, Reducing health disparities by tackling tobacco Use. J. Gen. Intern. Med. 32 (2017), pp. 961–962. DOI: 10.1007/s11606-017-4098-7.
- L. Rönnegård, Shen X and alam M. hglm: A package for fitting hierarchical generalized linear models. R. J. 2 (2010), pp. 20–28.
- Z. Shun, and P. McCullagh, Laplace approximation of high dimensional integrals. J Roy Stat Soc B Met. 57 (1995), pp. 749–760.
- U.S. Department of Health and Human Services. Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon General. Atlanta: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health 2012.
- Walker P, Whittaker C, Watson O, et al. Report 12: The global impact of COVID-19 and strategies for mitigation and suppression. 2020.
- C. Wang, L. Hsu, Z. Feng, et al., Regression calibration in failure time regression. Biometrics. 53 (1997), pp. 131–145.
- Y. Yasui, H. Liu, J. Benach, et al., An empirical evaluation of various priors in the empirical Bayes estimation of small area disease risks. Stat. Med. 19 (2000), pp. 2409–2420.
- Y. You, Small area estimation using area level models with model checking and applications. J Proceedings of Survey Methods Section, Statistical Society of Canada (2008).
- Y. You, and B. Chapman, Small area estimation using area level models and estimated sampling variances. Surv. Methodol. 32 (2006), pp. 97–103.
- X. Zhang, J.B. Holt, H. Lu, et al., Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the behavioral risk factor surveillance system. Am. J. Epidemiol. 179 (2014), pp. 1025–1033.