[
Atkinson, J., C. Salmond, and P. Crampton. 2019. NZ Dep 2013 index of deprivation interim research report. Technical report, Department of Public Health, University of Otago,Wellington. Available at: https://www.otago.ac.nz/wellington/otago730394.pdf. (accessed February 2022).
]Search in Google Scholar
[
Brown, J.J., C. Sexton, O. Abbott, and P.A. Smith. 2019. “The framework for estimating coverage in the 2011 census of England and Wales: Combining dual-system estimation with ratio estimation.” Statistical Journal of the International Association of Official Statistics 35: 481–499. DOI: https://doi.org/10.3233/SJI-180426.
]Search in Google Scholar
[
Bryant, J., K. Dunstan, P. Graham, N. Matheson-Dunning, E. Shrosbree, and R. Speirs. 2016. Measuring uncertainty in the 2013-base estimated resident population (Statistics New Zealand Working paper: 16–04). Wellington, New Zealand: Statistics New Zealand. Available at: https://www.stats.govt.nz/ (accessed February 2022).
]Search in Google Scholar
[
Chandrasekar, C. and W.E. Deming. 1949. “On a method of estimating birth and death rates and the extent of registration.” Journal of the American Statistical Association 44: 101–115. DOI: https://doi.org/10.1080/01621459.1949.10483294.
]Search in Google Scholar
[
Chen, C., J. Wakefield, and T. Lumley. 2014. “The use of sampling weights in Bayesian hierarchical models for small area estimation.” Spatial and Spatio-Temporal Epidemiology 11: 33–43. DOI: https://doi.org/10.1016/j.sste.2014.07.002.435736325457595
]Search in Google Scholar
[
Chen, S.X., C.Y. Tang, and V.T. Mule Jr. 2010. “Local post-stratification in dual system accuracy and coverage evaluation for the US census.” Journal of the American Statistical Association 105: 105–119. DOI: https://doi.org/10.1198/jasa.2009.ap08404.
]Search in Google Scholar
[
Chipperfield, J., J. Brown, and P. Bell. 2017. “Estimating the count error in the Australian census.” Journal of Official Statistics 33: 43–59. DOI: https://doi.org/10.1515/jos-2017-0003.
]Search in Google Scholar
[
Elliott, M.R. and R.J. Little. 2000. “A Bayesian approach to combining information from a census, a coverage measurement survey, and demographic analysis.” Journal of the American Statistical Association 95 (450): 351–362. DOI: https://doi.org/10.1080/01621459.2000.10474205.
]Search in Google Scholar
[
Gelman, A., J. Carlin, H. Stern, D. Dunson, D. Vehtari, and A. Rubin. 2014. Bayesian Data Analysis. Boca Raton, FL.: CRC Press.10.1201/b16018
]Search in Google Scholar
[
Gelman, A., and J. Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical models. Cambridge: Cambridge university press.10.1017/CBO9780511790942
]Search in Google Scholar
[
Gelman, A., A. Jakulin, M.G. Pittau, and Y.-S. Su. 2008. “A weakly informative default prior distribution for logistic and other regression models.” Annals of Applied Statistics 2: 1360–1383. DOI: https://doi.org/10.1214/08-A0AS191.
]Search in Google Scholar
[
Gelman, A. and T.C. Little. 1997. “Poststratification into many categories using hierarchical logistic regression.” Survey Methodology 23: 127–135. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/1997002/article/3616-eng.pdf?st=76F1g34m (accessed July 2022).
]Search in Google Scholar
[
Gelman, A., X.-L. Meng, and H. Stern. 1996. “Posterior predictive assessment of model fitness via realized discrepancies.” Statistica Sinica 6: 733–760.
]Search in Google Scholar
[
Ghitza, Y., and A. Gelman. 2013. “Deep interactions with MRP: Election turnout and voting patterns among small electoral subgroups.” American Journal of Political Science 57: 762–776. DOI: https://doi.org/10.1111/ajps.12004.
]Search in Google Scholar
[
Ghosh, M., K. Natarajan, T. Stroud, and B.P. Carlin. 1998. “Generalized linear models for small-area estimation.” Journal of the American Statistical Association 93: 273–282. DOI: https://doi.org/10.1080/01621459.1998.10474108.
]Search in Google Scholar
[
Hogan, H.P. 1993. “The 1990 post-enumeration survey: Operations and results.” Journal of the American Statistical Association 88: 1047–1060. DOI: https://doi.org/10.1080/01621459.1993.10476374.
]Search in Google Scholar
[
Lax, J.R., and J.H. Phillips. 2009. “How should we estimate public opinion in the States?” American Journal of Political Science 53: 107–121. DOI: https://doi.org/10.1111/j.1540-5907.2008.00360.x.
]Search in Google Scholar
[
Little, R.J. 2003. “The Bayesian approach to sample survey inference.” In Analysis of Complex Surveys, edited by R. Chambers and C. Skinner: 49–57. John Wiley and Sons.10.1002/0470867205.ch4
]Search in Google Scholar
[
Lumley, T., and A. Scott. 2017. “Fitting regression models to survey data.” Statistical Science 32: 265–278. DOI: https://doi.org/10.1214/16-STS605.
]Search in Google Scholar
[
Molina, I., B. Nandram, and J. Rao. 2014. “Small area estimation of general parameters with application to poverty indicators: a hierarchical Bayes approach.” Annals of Applied Statistics 8: 852–885. DOI: https://doi.org/10.1214/13-A0AS702.
]Search in Google Scholar
[
Mule, T., T. Schellhamer, D. Malec, and J. Maples. 2008. “Using continuous variables as modeling covariates for net coverage estimation.” In JSM Proceedings: Section on Survey Research Methods: 1941–1948. Denver. Available at: http://www.asasrms.org/Proceedings/y2008/Files/301279.pdf (accessed February 2022).
]Search in Google Scholar
[
Nandram, B., L. Chen, and B. Manandhar. 2018. “Bayesian analysis of multinomial counts from small areas and sub-areas.” In JSM proceedings: Section on Survey Research Methods: 1140–1162. Vancouver. Available at: http://www.asasrms.org/Proceedings/y2018/files/867100.pdf (accessed February 2022).
]Search in Google Scholar
[
Paige, J., G.-A. Fuglstad, A. Riebler, and J. Wakefield. 2020. “Design-and model-based approaches to small-area estimation in a low and middle income country context: comparisons and recommendations.” Journal of Survey Statistics and Methodology. DOI: https://doi.org/10.1093/jssam/smaa011.
]Search in Google Scholar
[
Pavlou, M., G. Ambler, S. Seaman, and R.Z. Omar. 2015. “A note on obtaining correct marginal predictions from a random intercepts model for binary outcomes.” BMC Medical Research Methodology 15: 1–6. DOI: http://doi.org/10.1186/s12874-015-0046-6.10.1186/s12874-015-0046-6452575126242875
]Search in Google Scholar
[
Pfeffermann, D. 2013. “New important developments in small area estimation.” Statistical Science 28: 40–68. DOI: https://doi.org/10.1214/12-STS395.
]Search in Google Scholar
[
Pfeffermann, D., F.A.D.S. Moura, and P.L.D.N. Silva. 2006. “Multilevel modelling under informative sampling.” Biometrika 93: 943–959. DOI: https://doi.org/10.1093/biomet/93.4.943.
]Search in Google Scholar
[
R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: https://www.R-project.org/ (accessed February 2022).
]Search in Google Scholar
[
Rabe-Hesketh, S., and A. Skrondal. 2006. “Multilevel modelling of complex survey data.” Journal of the Royal Statistical Society: 169: 805–827. DOI: https://doi.org/10.1111/j.1467-985X.2006.00426.x.
]Search in Google Scholar
[
Rao, J. and I. Molina. 2014. Small-area estimation. Hoboken, NJ: John Wiley & Sons, Inc.10.1002/9781118735855
]Search in Google Scholar
[
Rao, J., F. Verret, and M.A. Hidiroglou. 2013. “A weighted composite likelihood approach to inference for two-level models from survey data.” Survey Methodology 39(2): 263–282. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/12-001-x2013002-eng.pdf?st=LsDJmvSV (accessed July 2022).
]Search in Google Scholar
[
Rubin, D.B. 1987. Multiple imputation for nonresponse in surveys. Hoboken, NJ: John Wiley & Sons.10.1002/9780470316696
]Search in Google Scholar
[
Shirley, K.E., and A. Gelman. 2015. “Hierarchical models for estimating state and demographic trends in US death penalty public opinion.” Journal of the Royal Statistical Society 178: 1–28. DOI: https://doi.org/10.1111/rssa.12052.
]Search in Google Scholar
[
Si, Y., R. Trangucci, J.S. Gabry, and A. Gelman. 2020. “Bayesian hierarchical weighting adjustment and survey inference.” Survey Methodology 46: 181–214. Available at: https://www150.statcan.gc.ca/n1/pub/12-001-x/2020002/article/00003-eng.htm. (accessed July 2022).
]Search in Google Scholar
[
Skrondal, A., and S. Rabe-Hesketh. 2009. “Prediction in multilevel generalized linear models.” Journal of the Royal Statistical Society: 172: 659–687. DOI: https://doi.org/10.1111/j.1467-985X.2009.00587.x.
]Search in Google Scholar
[
Stan Development Team. 2020a. R Stan: the R interface to Stan. R package version 2.21.2. Available at: http://mc-stan.org/ (accessed February 2022). Stan Development Team. 2020b.
]Search in Google Scholar
[
Stan Modeling Language Users Guide and Reference Manual, version 2.25. Available at: http://mc-stan.org/ (accessed February 2022).
]Search in Google Scholar
[
Stats NZ. 2014. Coverage in the 2013 Census based on the New Zealand 2013 Post-enumeration Survey. Wellington: Statistics New Zealand. Available at: https://www.stats.govt.nz/. (accessed February 2022).
]Search in Google Scholar
[
Stats NZ. 2019. Overview of statistical methods for adding admin records to the 2018 Census dataset. Wellington, NZ: Statistics New Zealand. Available at: https://www.stats.govt.nz/ (accessed February 2022).
]Search in Google Scholar
[
Stats NZ 2020a. Estimated resident population 2018: Data sources and methods. Wellington, NZ: Statistics New Zealand. Available at: https://www.stats.govt.nz/ (accessed February 2022).
]Search in Google Scholar
[
Stats NZ. 2020b. Post-enumeration survey 2018: Methods and Results. Wellington, NZ: Statistics New Zealand. Available at: https://www.stats.govt.nz/ (accessed February 2022).
]Search in Google Scholar
[
Vehtari, A., A. Gelman, and J. Gabry. 2017. “Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC”. Statistics and Computing 27: 1413–1432. DOI: https://doi.org/10.1007/s11222-016-9696-4.
]Search in Google Scholar
[
Yi, G.Y., J. Rao, and H. Li. 2016. “A weighted composite likelihood approach for analysis of survey data under two-level models.” Statistica Sinica 26: 569–587. DOI: https://doi.org/10.5705/ss.2013.383.
]Search in Google Scholar
[
You, Y., and B. Chapman. 2006. “Small area estimation using area level models and estimated sampling variances.” Survey Methodology 32: 97–104. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2006001/article/9263-eng.pdf?st=a4rH5VTf (accessed July 2022).
]Search in Google Scholar
[
You, Y. and P. Dick. 2004. “Hierarchical Bayes small area inference to the 2001 census undercoverage estimation.” In JSM Proceedings: Section on Government Statistics: 1836–1840. Available at: http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000377.pdf (accessed February 2022).
]Search in Google Scholar