Estimating confidence intervals for spatial hierarchical mixed-effects models with post-stratification

Antal, 2011, A direct bootstrap method for complex sampling designs from a finite population, J. Amer. Statist. Assoc., 106, 534, 10.1198/jasa.2011.tm09767 Antal, 2011, Simple random sampling with over-replacement, J. Statist. Plann. Inference, 141, 597, 10.1016/j.jspi.2010.06.029 Buckland, 1984, Monte Carlo confidence intervals, Biometrics, 40, 811, 10.2307/2530926 Chatterjee, 2008, Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models, Ann. Statist., 36, 1221, 10.1214/07-AOS512 Diciccio, 1992, More accurate confidence intervals in exponential families, Biometrika, 79, 231, 10.1093/biomet/79.2.231 Dong, 2021, Modeling and presentation of vaccination coverage estimates using data from household surveys, Vaccine, 39, 2584, 10.1016/j.vaccine.2021.03.007 Dwyer-Lindgren, 2017, Trends and patterns of differences in chronic respiratory disease mortality among US counties, 1980–2014, JAMA, 318, 1136, 10.1001/jama.2017.11747 Eberth, 2018, Estimating county-level tobacco use and exposure in South Carolina: a spatial model-based small area estimation approach, Ann. Epidemiol., 28, 481, 10.1016/j.annepidem.2018.03.015 Efron, 1987, Better bootstrap confidence intervals, J. Amer. Statist. Assoc., 82, 171, 10.1080/01621459.1987.10478410 Efron, 1992, Bootstrap methods: another look at the jackknife, 569 Eke, 2016, Predicting periodontitis at state and local levels in the united states, J. Dent. Res., 95, 515, 10.1177/0022034516629112 Gelman, 1997, Poststratification into many categories using hierarchical logistic regression, Surv. Methodol., 23, 127 Hall, 2006, On parametric bootstrap methods for small area prediction, J. R. Stat. Soc. Ser. B Stat. Methodol., 68, 221, 10.1111/j.1467-9868.2006.00541.x Hao, 2015, Ozone, fine particulate matter, and chronic lower respiratory disease mortality in the United States, Am. J. Respir. Crit. Care Med., 192, 337, 10.1164/rccm.201410-1852OC Holt, 1979, Post stratification, J. R. Stat. Soc. Ser. A (Gen.), 142, 33, 10.2307/2344652 Jiang, 2001, Empirical best prediction for small area inference with binary data, Ann. Inst. Statist. Math., 53, 217, 10.1023/A:1012410420337 Jiang, 2006, Mixed model prediction and small area estimation, Test, 15, 1, 10.1007/BF02595419 Jiang, 2002, A unified jackknife theory for empirical best prediction with M-estimation, Ann. Statist., 30, 1782, 10.1214/aos/1043351257 Lahiri, 2003, On the impact of bootstrap in survey sampling and small-area estimation, Statist. Sci., 199 Little, 1993, Post-stratification: a modeler’s perspective, J. Amer. Statist. Assoc., 88, 1001, 10.1080/01621459.1993.10476368 Molina, 2009, Bootstrap for estimating the MSE of the spatial EBLUP, Comput. Statist., 24, 441, 10.1007/s00180-008-0138-4 Pfeffermann, 1996, The use of sampling weights for survey data analysis, Stat. Methods Med. Res., 5, 239, 10.1177/096228029600500303 Pfeffermann, 2012, Empirical bootstrap bias correction and estimation of prediction mean square error in small area estimation, Biometrika, 99, 457, 10.1093/biomet/ass010 Preacher, 2012, Advantages of Monte Carlo confidence intervals for indirect effects, Commun. Methods Meas., 6, 77, 10.1080/19312458.2012.679848 Rao, 2014, Small-area estimation, Wiley StatsRef: Stat. Ref. Online, 1, 1 Rasmussen, 2004, Modelling of discrete spatial variation in epidemiology with SAS using GLIMMIX, Comput. Methods Programs Biomed., 76, 83, 10.1016/j.cmpb.2004.03.003 Valliant, 1993, Poststratification and conditional variance estimation, J. Amer. Statist. Assoc., 88, 89 Zhang, 2014, 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, 1025, 10.1093/aje/kwu018 Zhang, 2015, Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the behavioral risk factor surveillance system, Am. J. Epidemiol., 182, 127, 10.1093/aje/kwv002