Small Area with Multiply Imputed Survey Data
By: Marina Runge and Timo Schmid
References
- Battese, G., Harter, R., and W. Fuller. 1988. “An error-components model for prediction of county crop areas using survey and satellite data.” Journal of The American Statistical Association 83: 28–36. DOI: https://doi.org/10.1080/01621459.1988.10478561.
- Benavent, R., and D. Morales. 2016. “ Multivariate Fay Herriot models for small area estimation.” Computational Statistics and Data Analysis 94: 372–390. DOI: https://doi.org/10.1016/j.csda.2015.07.013.
- Bijlsma, I., Brakel, J., R. van der Velden, and J. Allen. 2020. “Literacy Levels at a Detailed Regional Level: An Application Using Dutch Data.” Journal of Official Statistics 36: 251–274. DOI: https://doi.org/10.10.2478/jos-2020-0014.
- Casas-Cordero, C., J. Encina, and P. Lahiri. 2016. Poverty Mapping for the Chilean Comunas: 379–404. DOI: https://doi.org/10.10.1002/9781118814963.ch20.
- Chandra, H., K. Aditya, and S. Kumar. 2017. “Small area estimation under a log transformed area level model.” Journal of Statistical Theory and Practice 12: 497–505. DOI: https://doi.org/10.10.1080/15598608.2017.1415174.
- Citro, C.F., and G. Kalton. 2000. Small-Area Estimates of School-Age Children in Poverty: Evaluation of Current Methodology. Washington, DC: The National Academies Press. DOI: https://doi.org/10.10.17226/10046.
- Destatis. 2018. Wirtschaftsrechnungen Einkommens-und Verbrauchsstichsprobe Einkommensverteilung in Deutschland 2013. Statistisches Bundesamt (Destatis). Available at: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Einkommen-Konsum-Lebensbedingungen/Einkommen-Einnahmen-Ausgaben/Publikationen/Downloads-Einkommen/einkommensverteilung-2152606139004.pdf?__blob=publicationFile.
- European Commission. 2017. Taxation and customs, taxes in europe database v3. Available at: https://ec.europa.eu/taxation_customs/tedb/taxSearch.html (accessed February 2023).
- Eurostat. 2011a. Conventional dwellings by occupancy status, type of building and nuts 3 region. Available at: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=cens_11dwob_r3&lang=en (accessed April 2021).
- Eurostat. 2011b. Private households by type, tenure status and nuts 2 region. Available at: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=cens_11htts_r2&lang=en (accessed April 2021).
- Eurostat. 2017a. Harmonised unemployment rates. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ei_lmhr_m&lang=en (accessed April 2021).
- Eurostat. 2017b. Main gdp aggregates per capita. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10_pc&lang=en (accessed April 2021).
- Eurostat. 2017c. Main national accounts tax aggregates. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gov_10a_taxag&lang=en (accessed April 2021).
- Eurostat. 2017d. Population: Structure indicators. Available at: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_pjanind (accessed April 2021).
- Fay, R.E., and R.A. Herriot. 1979. “Estimates of income for small places: An application of James-Stein procedures to census data.” Journal of the American Statistical Association 74: 269–277. DOI: https://doi.org/10.10.2307/2286322.
- Fisher, J. 2006. Income imputation and the analysis of expenditure data in the consumer expenditure survey. U.S. Bureau of Labor Statistics, Working Articles. Available at: https://www.bls.gov/opub/mlr/2006/11/art2full.pdf (accessed October 2020).
- Gonzalez-Manteiga, W., Lombardia, M.J., I. Molina, D. Morales, and L. Santamaria. 2005. “Analytic and bootstrap approximations of prediction errors under a multivariate Fay-Herriot model.” Computational Statistics and Data Analysis 52: 5242–5252. DOI: https://doi.org/10.10.1016/j.csda.2008.04.031.
- Hadam, S., N. Würz., A.-K. Kreutzmann, and T. Schmid. 2023. “Estimating regional unemployment with mobile network data for functional urban areas in Germany.” Statistical Methods & Applications DOI: https://doi.org/10.1007/s10260-023-00722-0.
- Han, B. 2013. “Conditional Akaike information criterion in the Fay-Herriot model.” Statistical Methodology 11: 53–67. DOI: https://doi.org/10.10.1016/j.stamet.2012.09.002.
- HFCN. 2020a. The household finance and consumption survey: Methodological report for the 2017 wave. Household Finance and Consumption Network. Available at: https://www.ecb.europa.eu/pub/pdf/scpsps/ecb.sps35~b9b07dc66d.en.pdf (accessed April 2020).
- HFCN. 2020b. The household finance and consumption survey wave 2017 statistical tables. Household Finance and Consumption Network. Available at: https://www.ecb.europa.eu/home/pdf/research/hfcn/HFCS_Statistical_Tables_Wave_2017_May2021.pdf?ca15e575b6b7765dad1147e7a3dba728 (accessed April 2020).
- Jiang, J., Lahiri, P., S.-M. Wan, and C.-H. Wu. 2001. “Jackknifing in the Fay-Herriot model with an example. “In Proceedings og the seminar.” Funding Opportunity in Survey Research, Washington D.C., U.S. Bureau of Labor Statistics. Available at: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=70c03a8b572a00931735409d7173b087645006b9 (accessed April 2020).
- Jiang, J., and J. Rao. 2020. “Robust small area estimation: An overview.” Annual Review of Statistics and Its Application 7: 337–360. DOI: https://doi.org/10.10.1146/annurev-statistics-031219-041212.
- Kreutzmann, A.-K., P. Marek, M. Runge, N. Salvati, and T. Schmid. 2022. “The Fay-Herriot model for multiply imputed data with an application to regional wealth estimation in Germany.” Journal of Applied Statistics 49: 3278–3299. DOI: https://doi.org/10.10.1080/02664763.2021.1941805.
- Kreutzmann, A.-K., Pannier, S., N. Rojas-Perilla, T. Schmid, M. Templ, and N. Tzavidis. 2019. “The R package emdi for estimating and mapping regionally disaggregated indicators.” Journal of Statistical Software 91: 1–33. DOI: https://doi.org/10.10.18637/jss.v091.i07.
- Lahiri, P. and J.B. Suntornchost. 2015. “Variable selection for linear mixed models with applications in small area estimation.” The Indian Journal of Statistics 77: 312–320. DOI: https://doi.org/10.10.1007/s13571-015-0096-0.
- Longford, N. 2004. “Missing data and small area estimation in the uk labour force survey.” Journal of the Royal Statistical Society 167: 341–373. DOI: https://doi.org/10.10.1046/j.1467-985X.2003.00728.x.
- Longford, N.T. 2005. Missing data and small-area estimation. Springer, London.
- Maiti, T., H. Ren, and S. Sinha. 2014. “Prediction error of small area predictors shrinking both means and variances.” Scandinavian Journal of Statistics 41: 775–790. DOI: https://doi.org/10.10.1111/sjos.12061.
- Marhuenda, Y., D. Morales, and M. Pardo. 2014.“Information criteria for Fay-Herriot model selection.” Computational Statistics and Data Analysis 70: 268–280. DOI: https://doi.org/10.10.1016/j.csda.2013.09.016.
- Neves, A., D. Silva, and S. Correa. 2013. “Small domain estimation for the brazilian service sector survey.” Estadistica 65: 13–37. Available at: https://www.statistics.gov.hk/wsc/CPS003-P7-S.pdf (accessed November 2019).
- Pfeffermann, D. 2013. “New important developments in small area estimation.” Statistical Science 28: 40–68. DOI: https://doi.org/10.10.1214/12-STS395.
- Prasad, N., and J. Rao. 1990. “The estimation of the mean squared error of small-area estimators.” Journal of the American Statistical Association 85: 163–171. DOI: https://doi.org/10.10.2307/2289539.
- R Core Team. 2020. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/.
- Rao, J., and M. Yu. 1994. “Small area estimation by combining time series and cross sectional data.” Canadian Journal of Statistics 22: 511–528. DOI: https://doi.org/10.10.2307/3315407.
- Rao, J.N.K. and Molina. 2015. Small Area Estimation. John Wiley and Sons, Hoboken.
- Riphahn, R. and O. Serfling. 2005. “Item non-response on income and wealth questions.” Empirical Economics 30: 521–538. DOI: https://doi.org/10.10.1007/s00181-005-0247-7.
- Rivest, L.-P., and N. Vandal. 2002. “Mean squared error estimation for small areas when the small area variances are estimated.” In Proceedings of International Conference of Recent Advanced Survey Sampling, edited by J. Rao, July 10–13, Ottawa, Canada: 197–206. Available at: https://www.mat.ulaval.ca/fileadmin/mat/documents/lrivest/-Publications/64-RivestVandal2003.pdf (accessed October 2020).
- Rubin, D.B. 1976. “Inference and missing data.” Biometrika 63: 163–171. DOI: https://doi.org/10.10.2307/2335739.
- Rubin, D.B. 1987. Multiple imputation for nonresponse in surveys. John Wiley and Sons, Hoboken.
- Rubin, D.B. 1996. “Multiple imputation after 18 þ years.” Journal of the American Statistical Society 91: 473–489. DOI: https://doi.org/10.10.2307/2291635.
- Schmid, T., F. Bruckschen, N. Salvati, and T. Zbiranski. 2017. “Constructing sociodemographic indicators for national statistical institutes by using mobile phone data: estimating literacy rates in Senegal.” Journal of the Royal Statistical Society 180: 1163–1190. DOI: https://doi.org/10.10.1111/rssa.12305.
- Slud, E., and T. Maiti. 2006. “Mean-squared error estimation in transformed Fay-Herriot models.” Journal of the Royal Statistical Society 68: 239–257. DOI: https://doi.org/10.10.1111/j.1467-9868.2006.00542.x.
- Sugasawa, S., and T. Kubokawa. 2017. “Transforming response values in small area prediction.” Computational Statistics and Data Analysis 114: 47–60. DOI: https://doi.org/10.10.1016/j.csda.2017.03.017.
- Sugasawa, S., H. Tamae, and T. Kubokawa. 2017. “Bayesian estimators for small area models shrinking both means and variances.” Scandinavian Journal of Statistics 44: 150–167. DOI: https://doi.org/10.10.1111/sjos.12246.
- Sverchkov, M., and D. Pfaeffermann. 2018. “Small area estimation under informative sampling and not missing at random non-response.” Journal of the Royal Statistical Society: 981–1008. DOI: https://doi.org/10.10.1111/rssa.12362.
- Tzavidis, N., l.-C. Zhang, A. Luna, T. Schmid, and N. Rojas-Perilla. 2018. “From start to finish: a framework for the production of small area official statistics.” Journal of the Royal Statistical Society 181: 927–979. DOI: https://doi.org/10.10.1111/rssa.12364.
- Vaida, F., and S. Blanchard. 2005. “Conditional akaike information for mixed-effects models.” Biometrika 92: 351–370.
- Van Buuren, S. 2018. Flexible Imputation of Missing Data. Second Edition. Chapman and Hall/CRC.
- Van Buuren, S., and K. Groothius-Oudshoorn. 2011. “mice: Multivariate imputation by chained equation in R.” Journal of Statistical Software 45: 1–67. DOI: https://doi.org/10.10.18637/jss.v045.i03.
- Wang, J., and W.A. Fuller. 2003. “The mean squared error of small area predictors constructed with estimated area variances.” Journal of the American Statistical Association 98: 716–723. DOI: https://doi.org/10.10.1198/016214503000000620.
- You, Y., and B. Chapman. 2006. “Small area estimation using area level models and estimated sampling variances.” Survey Methodology 32: 97–103. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019263 (accessed March 2020).
DOI: https://doi.org/10.2478/jos-2023-0024 | Journal eISSN: 2001-7367
Language: English
Page range: 507 - 533
Submitted on: Nov 1, 2022
Accepted on: Jun 1, 2023
Published online: Dec 10, 2023
Published in print: Dec 1, 2023
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Related subjects:
© 2023 Marina Runge, Timo Schmid, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.