Open Access

Small Area Estimation with a Lognormal Mixed Model under Informative Sampling

Journal of Official Statistics's Cover Image
Journal of Official Statistics
Special Issue on Establishment Surveys (ICES-V)

Cite

Alfons, A., M. Templ, and P. Filzmoser. 2010. “An Object-Oriented Framework for Statistical Simulation: The R Package simFrame.” Journal of Statistical Software 37: 1–36. Doi: http://dx.doi.org/10.18637/jss.v037.i03.10.18637/jss.v037.i03Search in Google Scholar

Asparouhov, T. 2006. “General Multi-Level Modeling with Sampling Weights.” Communications in Statistics Theory and Methods 35: 439–460. Doi: http://dx.doi.org/10.1080/03610920500476598.10.1080/03610920500476598Open DOISearch in Google Scholar

Battese, G.E., R.M. Harter, and W.A. Fuller. 1988. “An Error Component Model for Prediction of County Crop Areas Using Survey and Satellite Data.” Journal of the American Statistical Association 83: 28–36. Doi: http://dx.doi.org/10.1080/01621459.1988.10478561.10.1080/01621459.1988.10478561Open DOISearch in Google Scholar

Berg, E. and H. Chandra. 2014. “Small Area Prediction for a Unit-Level Lognormal Model.” Computational Statistics & Data Analysis 78: 159–175. Doi: http://dx.doi.org/10.1016/j.csda.2014.03.007.10.1016/j.csda.2014.03.007Open DOISearch in Google Scholar

Bernardini Papalia, R., C. Bruch, T. Enderle, S. Falorsi, A. Fasulo, E. Hernandez-Vazquez, M. Ferrante, J. Kolb, R. Münnich, S. Pacei, R. Priam, P. Righi, T. Schmid, N. Shlomo, F. Volk, T. Zimmermann, and S. Zins. 2013. Best Practice Recommendations on Variance Estimation and Small Area Estimation in Business Surveys. Technical report, SSH-CT-2010-244767-BLUE-ETS. Available at: http://www.blue-ets.istat.it/fileadmin/deliverables/Deliverable6.2.pdf (accessed on 12 September 2017).Search in Google Scholar

Burgard, J.P., R. Münnich, and T. Zimmermann. 2014. “The Impact of Sampling Designs on Small Area Estimates for Business Data.” Journal of Official Statistics 30: 749–771. Doi: http://dx.doi.org/10.2478/jos-2014-0046.10.2478/jos-2014-0046Open DOISearch in Google Scholar

Chandra, H. and R. Chambers. 2011. “Small Area Estimation under Transformation to Linearity.” Survey Methodology 37: 39–51.Search in Google Scholar

Ferrante, M.R., C. Trivisano, and E. Fabrizi. 2016. “Bayesian Small Area Estimation Methods for Business Survey Statistics.” In Proceedings of the 60th World Statistics Congress of the International Statistical Institute, 26–31 July 2015, 86–91, Rio de Janeiro.Search in Google Scholar

Hidiroglou, M.A. and P. Lavallee. 2009. “Sampling and Estimation in Business Surveys.” In Handbook of Statistics, Volume 29 A, edited by D. Pfeffermann and C.R. Rao, Chapter 17, 441–470. Elsevier.10.1016/S0169-7161(08)00017-5Search in Google Scholar

Hidiroglou, M.A. and P. Smith. 2005. “Developing Small Area Estimates for Business Surveys at the ONS.” Statistics in Transition 7: 527–539.Search in Google Scholar

Jiang, J., P. Lahiri, and S.-M. Wan. 2002. “A Unified Jackknife Theory for Empirical Best Prediction with M-Estimation.” The Annals of Statistics 30: 1782–1810. Doi: http://dx.doi.org/10.1214/aos/1043351257.10.1214/aos/1043351257Open DOISearch in Google Scholar

Krieg, S., V. Blaess, and M. Smeets. 2012. “Small Area Estimation of Turnover of the Structural Business Survey.” Discussion paper 201203, Statistics Netherlands. Available at: https://www.cbs.nl//media/imported/documents/2012/07/2012-03-x10-pub.pdf (accessed on 12 September 2017).Search in Google Scholar

Pfeffermann, D. 2013. “New Important Developments in Small Area Estimation.” Statistical Science 28: 40–68. Doi: http://dx.doi.org/10.1214/12-STS395.10.1214/12-STS395Open DOISearch in Google Scholar

Pfeffermann, D. and C.R. Rao. 2009a. Handbook of Statistics: Sample Surveys: Design, Methods and Applications, Volume 29A. Elsevier.Search in Google Scholar

Pfeffermann, D. and C.R. Rao. 2009b. Handbook of Statistics: Sample Surveys: Inference and Analysis, Volume 29A. Elsevier.Search in Google Scholar

Pfeffermann, D. and M. Sverchkov. 2007. “Small-Area Estimation under Informative Probability Sampling of Areas and within the Selected Areas.” Journal of the American Statistical Association 102: 1427–1439. Doi: http://dx.doi.org/10.1198/016214507000001094.10.1198/016214507000001094Open DOISearch in Google Scholar

Pfeffermann, D. and M. Sverchkov. 2009. “Inference under Informative Sampling.” In Handbook of Statistics, Volume 29B, edited by D. Pfeffermann and C.R. Rao, Chapter 39, 455–487. Elsevier.10.1016/S0169-7161(09)00239-9Search in Google Scholar

Prasad, N.G.N. and J.N.K. Rao. 1999. “On Robust Small Area Estimation Using a Simple Random Effects Model.” Survey Methodology 25: 67–72.Search in Google Scholar

Rao, J.N.K. and I. Molina. 2015. Small Area Estimation. Hoboken, NJ: John Wiley &Sons. Doi:10.1002/9781118735855.10.1002/9781118735855Open DOISearch in Google Scholar

Shapiro, S.S. and M.B. Wilk. 1965. “An Analysis of Variance Test for Normality (Complete Samples).” Biometrika 52: 591–611. Doi: http://dx.doi.org/10.2307/2333709.10.2307/2333709Open DOISearch in Google Scholar

Skinner, C. 1994. “Sample Models and Weights.” In Proceedings of the Section on Survey Research Methods: American Statistical Association, 13–18 August 1994. 133–142. Toronto. Available at: http://ww2.amstat.org/sections/srms/Proceedings/papers/1994_018.pdf (accessed on 12 September 2017).Search in Google Scholar

Tillé, Y. 2006. Sampling Algorithms, Springer Series in Statistics. New York: Springer.Search in Google Scholar

Vaida, F. and S. Blanchard. 2005. “Conditional Akaike Information for Mixed-Effects Models.” Biometrika 92: 351–370. Doi: http://dx.doi.org/10.1093/biomet/92.2.351.10.1093/biomet/92.2.351Open DOISearch in Google Scholar

Valliant, R., A.H. Dorfman, and R.M. Royall. 2000. Finite Population Sampling and Inference: a Prediction Approach. John Wiley.Search in Google Scholar

Verret, F., M.A. Hidiroglou, and J.N.K. Rao. 2015. “Model-Based Small Area Estimation under Informative Sampling.” Survey Methodology 41: 333–347.Search in Google Scholar

You, Y. and J.N.K. Rao. 2002. “A Pseudo-Empirical Best Linear Unbiased Prediction Approach to Small Area Estimation Using Survey Weights.” The Canadian Journal of Statistics 30: 431–439. Doi: http://dx.doi.org/10.2307/3316146.10.2307/3316146Open DOISearch in Google Scholar

Zimmermann, T. 2018. The Interplay between Sampling Design and Statistical Modelling in Small Area Estimation. PhD thesis, University of Trier.Search in Google Scholar

eISSN:
2001-7367
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Mathematics, Probability and Statistics