PL EN


2016 | 17 | 1 | 105-132
Article title

Development of Small Area Estimationin Official Statistics

Authors
Content
Title variants
Languages of publication
EN
Abstracts
EN
The author begins with a general assessment of the mission of the National Statistics Institutes (NSIs), main producers of official statistics, which are obliged to deliver high quality statistical information on the state and evolution of the population, the economy, the society and the environment. These statistical results must be based on scientific principles and methods. They must be made available to the public, politics, economy and research for decision-making and information purposes. Next, before discussing general issues of small area estimation (SAE) in official statistics, the author reminds: the methods of sampling surveys, data collection, estimation procedures, and data quality assessment used for official statistics. Statistical information is published in different breakdowns with stable or even decreasing budget while being legally bound to control the response burden. Special attention is paid, from a practitioner point of view, to synthetic development of small area estimation in official statistics, beginning with international seminars and conferences devoted to SAE procedures and methods (starting with the Canadian symposium, 1985, and the Warsaw conference, 1992, to the Poznan conference, Poland, 2014), and some international projects (EURAREA, SAMPLE, BIAS, AMELI, ESSnet). Next, some aspects of development of SAE in official statistics are discussed. At the end some conclusions regarding quality of SAE procedures are considered.
Year
Volume
17
Issue
1
Pages
105-132
Physical description
Contributors
author
  • Central Statistical Office of Poland and Warsaw Management Academy
References
  • AUSTRALIAN BUREAU OF STATISTICS, (2006). A Guide to Small Area Estimation – Version 1.1. Internal ABS document. available online at: http://www.nss.gov.au/nss/home.NSF/pages/Small+Areas+Estimates?OpenDocu.
  • BATTESE, G. E., HARTER, R. M., FULLER, W. A., (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.
  • BEST, N., RICHARDSON, S., THOMSON, A., (2005). A comparison of Bayesian spatial models for disease mapping. Statistical Methods in Medical Research, 14, 35–39.
  • BETHLEHEM, J. G., (1988). Reduction of nonresponse bias through regression estimation. Journal of Official Statistics, 4, 251–260.
  • BETHLEHEM, J. G., KERSTEN, H. M. P., (1985). On the treatment of nonresponse in sample surveys. Journal of Official Statistics, 1, 287–300.
  • BRACHA, CZ., LEDNICKI, B., WIECZORKOWSKI, R., (2003). Estimation of Data from the Polish Labour Force Surveys by poviats (counties) in 1995–2002 (in Polish), Central Statistical Office of Poland, Warsaw.
  • BRACKSTONE, G. J., (2002). Strategies and Approaches for Small Area Statistics, Survey Methodology, 28(2), 117–123.
  • BRACKSTONE, H., (1999). Managing Data Quality in a Statistical Agency. Survey Methodology, 25, 2, 129–149.
  • BRAKEL, J. A. VAN DEN, BETHLEHEM, J., (2008). Model-Based Estimation for Official Statistics, Statistics Netherlands, Voorburg/Heerlen.
  • CHAMBERS, R., BRAKEL, J. A. VAN DEN, HEDLIN, D., LEHTONEN, R., ZHANG, LI-CHUN, (2006). Future Challenges of Small Area Estimation. Statistics in Transition, 7, 759–769.
  • CHOUDRY, G. H., RAO, J. N. K., (1989). Small area estimation using models that combine time series and cross sectional data, in: Singh, A.C., Whitridge, P. (Eds.), Proceedings of Statistics Canada Symposium on Analysis of Data in Time, pp. 67–74.
  • CHOUDHRY, G. H., RAO, J. N. K., HIDIROGLOU, M. A., (2012). On sample allocation for efficient domain estimation. Survey Methodology, 38, 23–29.
  • COCHRAN, W. G., (1977). Sampling Techniques, 3rd ed. New York: Wiley.
  • DATTA, G., (2009). Model-based approach to small area estimation. Chapter 32 in Rao C.R. and Pfeffermann D. (Eds.). Handbook of Statistics. Sample Surveys: Inference and Analysis. Vol. 29B. New York: Elsevier. (251-288).
  • DATTA, G. S., LAHIRI, P., MAITI, T., LU, K. L., (1999). Hierarchical Bayes estimation of unemployment rates for the U.S. states. Journal of the American Statistical Association, 94, 1074–1082.
  • DATTA, G. S., LAHIRI, P., MAITI, T., (2002). Empirical Bayes estimation of median income of four person families by state using time series and cross-sectional data. Journal of Statistical Planning and Inference, 102, 83–97.
  • DEHNEL, G., (2010). The development of micro-entrepreneurship in Poland in the light of the estimation for small areas, Publ. University of Economics in Poznan, Poznan (in Polish).
  • DEHNEL, G., GOLATA, E., KLIMANEK, T., (2004). Consideration on Optimal Design for Small Area Estimation, Statistics in Transition, vol. 6, Nr 5, pp. 725–754.
  • DEVILLE, J., SÄRNAL, C.-E., (1992). Calibration Estimators in Survey Sampling, Journal of the American Statistical Association, 87, 376–382.
  • DICK, P., (1995). Modelling net undercoverage in the 1991 Canadian census. Survey Methodology, 21, 45-54.
  • DREW, D., SINGH, M. P., CHOUDHRY, G. H., (1982). Evaluation of Small Area Estimation Techniques for the Canadian Labour Force Survey, Survey Methodology, 8, 17–47.
  • ELAZAR, D., (2004), Small Area Estimation of Disability in Australia, Statistics in Transition, 6, 5, 667–684.
  • ESTEBAN, M. D., MORALES, D., PEREZ, A., SANTAMARIA, L., (2012). Small area estimation of poverty proportions under area-level time models. Computational Statistics and Data Analysis, 56, 2840–2855.
  • EURAREA, (2004). Project reference volume, deliverable D7.1.4, Technical report, EURAREA consortium.
  • EUROSTAT, (2007). Handbook on Data Quality Assessment: Methods and Tools, Luxembourg.
  • FAY, R. E., HERRIOT, R. A., (1979). Estimation of income from small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association, 74, 269–277.
  • FULLER, W. A., (1999). Environmental surveys over time. Journal of the Agricultural, Biological and Environmental Statistics, 4, 331–345.
  • FULLER, W. A., (2002). Regression estimation for survey samples. Survey Methodology, 28, 5–23.
  • GAMBINO, J. G., Dick, P., (2000). Small Area Estimation Practice at Statistics Canada, Statistics in Transition, 4, 4, 597–610.
  • GAMBINO, J. G., SINGH, M. P., DUFOUR, J., KENNEDY, B., LINDEYER, J., (1998). Methodology of the Canadian Labour Force Survey, Statistics Canada.
  • GOLATA, E., (2004). Problems of Estimate Unemployment for Small Domains in Poland, Statistics in Transition, 6, 5. 755–776.
  • GOSH, M., MEEDEN, G., (1997). Bayesian Methods for Finite Population Sampling . London: Chapman & Hall.
  • GHOSH, M., NATARAJAN, K., WALTER, L. A., KIM, D. H., (1999). Hierarchical Bayes GLMs for the analysis of spatial data: An application to disease mapping. Journal of Statistical Planning and Inference, 75, 305–318.
  • GHOSH, M., RAO, J. N. K., (1994). Small Area Estimation: An Appraisal, Statistical Science, 9, 55–93.
  • GONZALEZ-MANTEIGA, W., LOMBARDIA, M. J., MOLINA, I., MORALES, D., SANTAMARIA, L., (2010). Small area estimation under Fay-Herriot models with nonparametric estimation of heteroscedasticity. Statistical Modelling, 10, 2, 215–239.
  • HANSEN, M. H., HURWITZ, W. N., MADOW. W. G., (1953). Sample Survey Methods and Theory, Vol. I and II. New York: Wiley.
  • HEADY, P., HENNELL, S., (2001). Enhancing Small Area Estimation Techniques to Meet European Needs, Statistics in Transition, 5, 2, 195–203.
  • HEADY, P., RALPHS, M., (2004). Some Findings of the EURAREA Project – and their Implications for Statistical Policy, Statistics in Transition, 6, 5, 641–654.
  • HIDIROGLOU, M. A., (2014). Small-Area Estimation: Theory and Practice, Section on Survey Research Methods, Statistics Canada.
  • HIDIROGLOU, M. A., SINGH A., HAMEL M., (2007). Some Thoughts on Small Area Estimation for the Canadian Community Health Survey (CCHS). Internal Statistics Canada document.
  • HIDIROGLOU, M. A., SÄRNDAL, C. E., (1985). Small Domain Estimation: A Conditional Analysis, Proceedings of the Social Statistics Section, American Statistical Association, 147–158.
  • HIDIROGLOU, M. A., PATAK, Z., (2004). Domain estimation using linear regression. Survey Methodology, 30, 67–78.
  • HOLT, D., ELLIOT, D., (1991). Methods of weighting for unit non-response. The Statistician, 40, 333–342.
  • HORVITZ, D. G., THOMPSON, D. J., (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.
  • JIANG, J., LAHIRI, P., (2006). Mixed model prediction and small area estimation. Test, 15, 1–96.
  • KALTON, G., (2002). Models in the Practice of Survey Sampling (Revisited), Journal of Official Statistics, 18, 129–154.
  • KALTON, G., KASPRZYK, D., (1986) The treatment of missing data. Survey Methodology, 12, 1–16.
  • KALTON, G., KORDOS, J., PLATEK, R., (1993). Small Area Statistics and Survey Designs, Vol. I: Invited Papers; Vol. II: Contributed Papers and Panel Discussion. Central Statistical Office, Warsaw.
  • KISH, L., (1965). Survey Sampling. New York: Wiley.
  • KORDOS, J., (1994). Small Area Statistics in Poland (Historical Review). Statistics in Transition, 1, 6,783–796.
  • KORDOS, J., (2005). Some Aspects of Small Area Statistics and Data Quality, Statistics in Transition, 7, 1, 63–83.
  • KORDOS, J., (2006). Impact of different factors on research in small area estimation in Poland, „Statistics in Transition”, 7, 4, 863–879.
  • KORDOS, J., PARADYSZ, J., (2000). Some Experiments in Small Area Estimation in Poland, Statistics in Transition, 4, 4, 679–697.
  • KUBACKI, J., (2004). Application of the Hierarchical Bayes Estimation to the Polish Labour Force Survey, Statistics in Transition, 6, 5, 785–796.
  • LEHTONEN, R., SÄRNDAL, C. E., VEIJANEN, A., (2003). The effect of model choice in estimation for domains, including small domains. Survey Methodology, 29, 33–44.
  • LEHTONEN, R., SÄRNDAL, C. E., VEIJANEN, A., (2005). Does the model matter? Comparing model-assisted and model-dependent estimators of class frequencies for domains. Statistics in Transition, 7, 649–673.
  • LEHTONEN, R., VEIJANEN, A., (2009). Design-based methods of estimation for domains and small areas. Chapter 31 in Rao C.R. and Pfeffermann D. (Eds.). Handbook of Statistics. Sample Surveys: Inference and Analysis. Vol. 29B. New York: Elsevier, 219–249.
  • LEHTONEN, R., SÄRNDAL, C. E., VEIJANEN, A., (2009). Model calibration and generalized regression estimation for domains and small areas. Invited paper, SAE2009 Conference on Small area estimation, 29 June – 1 July, 2009, Elche, Spain.
  • LITTLE, R. J. A., (2004). To Model or Not to Model? Competing Modes of Inference for Finite Population Sampling, Journal of the Royal Statistical Association, 99, 546–556.
  • LITTLE, R. J. A., (2012). Calibrated Bayes, an Alternative Inferential Paradigm for Official Statistics Journal of Official Statistics, 28, 3, 309–334.
  • LONGFORD, N., (2005). Missing Data and Small-Area Estimation: Modern Analytical Equipment for the Survey Statistician, Springer.
  • LUNDSTRÖM, S., SÄRNDAL, C. E., (1999). Calibration as a standard method for treatment of nonresponse. Journal of Official Statistics, 15, 305–327.
  • MARHUENDA, Y. MOLINA, I., MORALES, D., (2013). Small area estimation with spatio-temporal Fay-Herriot models. Computational Statistics and Data Analysis, 58, 308–325.
  • MARKER, D. A., (2001). Producing small area estimates from national surveys: Methods for minimizing use of indirect estimators. Survey Methodology, 27, 183–188.
  • MARSHALL, A., (updated by V. Higgins, 2013). Small area estimation using key UK surveys – An Introductory guide. UK Data Service, University of Essex and University of Manchester.
  • MOLINA, I., RAO, J. N. K., (2010). Small area estimation of poverty indicators, The Canadian Journal of Statistics, 38, 3, 369–385.
  • MOLINA, I., SALVATI, N., PRATESI, M., (2009). Bootstrap for estimating the MSE of the Spatial EBLUP. Computational Statistics, 24, 441–458.
  • MOURA, F. A. S., MIGON, H. S., (2002). Bayesian spatial models for small area proportions. Statistical Modelling, 2, 3, 183–201.
  • NARAIN, R., (1951). On sampling without replacement with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 3, 169–174.
  • OPSOMER, J. D., CLAESKENS, G., RANALLI, M. G., KAUERMANN, G., BREIDT, F. J., (2008). Nonparametric small area estimation using penalized spline regression. Journal of the Royal Statistical Society, B70, 265–286.
  • PARADYSZ, J., (1998). Small Area Statistics in Poland - First Experiences and Application Possibilities, Statistics in Transition, 3, 5, 1003–1015.
  • PARADYSZ, J., DEHNEL, G., (2005). Attempts to Estimate Basic Information for Small Business in Poland, SAE2005 Conference, Jyväskylä, Finland, 28–31 August 2005.
  • PLATEK, R., RAO, J. N. K. SÄRNDAL, C. E., SINGH, M. P. (Eds.), (1987). Small Area Statistics, John Wiley & Sons, New York.
  • PFEFFERMANN, D., (1999). Small area estimation – big developments. Proceedings of IASS Conference on Small Area Estimation, Riga. Latvian Council of Sciences, 129–145.
  • PFEFFERMANN, D., (2002). Small area estimation – new developments and directions. International Statistical Review 70, 125–143.
  • PFEFFERMANN, D., (2013). New important developments in small area estimation, Statistical Science, 28, 1, 40–68.
  • PFEFFERMANN, D., BURCK, L., (1990). Robust small area estimation combining time series and cross-sectional data. Survey Methodology, 16, 217–237.
  • PFEFFERMANN, D., Tiller, R., (2006). Small Area Estimation with State Space Models Subject to Benchmark Constraints. Journal of the American Statistical Association, 101, 1387–1897
  • PRASAD, N. G. N., RAO, J. N. K., (1990). “The Estimation of Mean Squared Error of Small-Area Estimators”, Journal of the American Statistical Association, 85, 163–171.
  • PRASAD, N. G. N., RAO, J. N. K, (1999). On robust small area estimation using a simple random effects model. Survey Methodology, 25, 67–72.
  • PRATESI, M., SALVATI, N., (2008). Small area estimation: the EBLUP estimator based on spatially correlated random area effects. Statistical Methods and Applications, 17, 113–141.
  • RAO, J. N. K., (1999). Some recent advances in model-based small area estimation, Survey Methodology, 25, 2, 175–186.
  • RAO, J. N. K., (2003). Small Area Estimation, John Wiley & Sons, New Jersey.
  • RAO, J. N. K., (2011). Impact of Frequentist and Bayesian Methods on Survey Sampling Practice: A Selective Appraisal, Statistical Science, 26, 2, 240–256.
  • RAO, J. N. K., YU, M., (1994). Small area estimation by combining time series and cross sectional data. Canadian Journal of Statistics, 22, 511–528.
  • RIGA, (1999). Small Area Estimation–Conference Proceedings, Riga, Latvia, August 1999.
  • SÄRNDAL, C. E., SWENSSON, B., WRETMAN, J., (1992). Model Assisted Survey Sampling. New York: Springer-Verlag.
  • SÄRNDAL, C.-E., LUNDSTRÖM, S., (2005). References, in Estimation in Surveys with Nonresponse, John Wiley & Sons.
  • SCHAIBLE, W. A., (1978). Choosing Weights for Composite Estimators for Small Area Statistics, Proceedings of the Section on Survey Research Methods, American Statistical Association, 741–746.
  • SCHAIBLE, W. L., CASADY, R. J., (1994). The Development, Application, and Evaluation of Small Area Estimators. Statistics in Transition, 1, 6, 727–746.
  • SINGH, M. P., GAMBINO, J., MANTEL, H. J., (1994). Issues and Strategies for Small Area Data, Survey Methodology, 20, 3–22.
  • SINGH, B., SHUKLA, G., KUNDU, D., (2005). Spatio-temporal models in small area estimation. Survey Methodology, 31, 183–195.
  • SZARKOWSKI, A., WITKOWSKI, J., (1994). The Polish Labour Force Survey, Statistics in Transition, 1, 4, 467–483.
  • THOMSEN, I., HOLMOY, A. M. K., (1998). Combining data from surveys and administrative record systems. The Norwegian experience, Inter. Satist. Rev., 66, 201–221.
  • TREWIN, D., (1999). Small Area Statistics Conference, Survey Statistician, 41, 8–9.
  • TREWIN, D., (2002). The importance of a Quality Culture, Survey Methodology, 28, 2, 125–133.
  • UN, (2011). Using Administrative and Secondary Sources for Official Statistics – A Handbook of Principles and Practices, United Nations Commission for Europe.
  • VALLIANT, R., DORFMAN, A. H., ROYALL, R. M., (2000). Finite Population Sampling and Inference, A Prediction Approach. New York: Wiley.
  • WANG, J., FULLER, W. A., (2003). The mean square error of small area predictors constructed with estimated area variances. Journal of the American Statistical Association, 98, 463, 716–723.
  • WANG, J., FULLER, W. A., QU, Y., (2008). Small area estimation under a restriction. Survey Methodology, 34, 1, 29–36.
  • WOODRUFF, R. S., (1966). Use of a Regression Technique to Produce Area Breakdowns of the Monthly National Estimates of Retail Trade, Journal of the American Statistical Association, 61, 496–504.
  • WYWIAŁ, J., (2000). On precision of Horvitz–Thompson strategies, Statistics in Transition, 4. 5, 779–798.
  • YATES, F., (1981). Sampling methods for censuses and surveys. 4th Ed., Charles Griffin and Co., London, U.K.
  • YOU, Y., (2008). An integrated modelling approach to unemployment rate estimation for sub-provincial areas of Canada. Survey Methodology, 34, 19–27.
  • YOU, Y., CHAPMAN, B., (2006). Small area estimation using area level models and estimated sampling variances. Survey Methodology, 32, 97–103.
  • YOU, Y., RAO, J. N. K., (2002). A pseudo empirical best linear unbiased prediction approach to small area estimation using survey weights. The Canadian Journal of Statistics, 30, 431–439.
  • YOU, Y., RAO, J. N. K., GAMBINO, J. G., (2003). Model-based unemployment rate estimation for the Canadian Labour Force Survey: A hierarchical Bayes approach, Survey Methodology, 29, 25–32.
  • YOU, Y, DICK, P., (2004). Hierarchical Bayes Small Area Inference to the 2001 Census Undercoverage Estimation. Proceedings of the ASA Section on Government Statistics, 1836–1840.
  • YOU, Y., ZHOU, Q. M., (2011). Hierarchical Bayes small area estimation under a spatial model with application to health survey data, Survey Methodology, 37, 1, 25–37.
  • WHITWORTH, A., (edt). Evaluations and improvements in small area estimation methodologies, University of Sheffield: http://eprints.ncrm.ac .uk/3210/1/sme_whitworth.pdf.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-29cb8e8f-3678-4c4e-98b6-71196e70ef16
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.