IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v196y2009i1p360-369.html
   My bibliography  Save this article

Preemptive benchmarking problem: An approach for official statistics in small areas

Author

Listed:
  • Sedeño-Noda, A.
  • González-Dávila, E.
  • González-Martín, C.
  • González-Yanes, A.

Abstract

National Statistical Agencies and Autonomous Institutions are extremely interested in using information from those areas that are actually smaller than the actual areas for which a survey is initially designed. As such, small area estimation and its application are valuable when conducting research on Official Statistics. A wide range of different methods are available which provide estimations to small area levels, being reasonable to guarantee that they add up to the published design-based estimations in a large area that includes these small areas. This requirement is known as benchmarking. Different algorithms, all based on distances between original data and modified data, are introduced in this paper, with the intention of satisfying the benchmarking property. We provide rules to apply these proposed calibrated methods according to user criteria. Goal programming with priorities methodology is used to represent user preferences. The result is a collection of different interdependent network flow problems. Some of these problems require the development of ad hoc methods. The introduced methods are assessed by a Monte Carlo simulation study using the Spanish Labour Force Survey in the Canary Islands. The results also show that the consistency of the estimator is independent of the used calibrated methods, but it does depend on the benchmarking weights.

Suggested Citation

  • Sedeño-Noda, A. & González-Dávila, E. & González-Martín, C. & González-Yanes, A., 2009. "Preemptive benchmarking problem: An approach for official statistics in small areas," European Journal of Operational Research, Elsevier, vol. 196(1), pages 360-369, July.
    • Handle: RePEc:eee:ejores:v:196:y:2009:i:1:p:360-369
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(08)00249-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    2. Ahuja, Ravindra K. & Kodialam, Murali & Mishra, Ajay K. & Orlin, James B., 1997. "Computational investigations of maximum flow algorithms," European Journal of Operational Research, Elsevier, vol. 97(3), pages 509-542, March.
    3. Ravindra K. Ahuja & James B. Orlin, 1991. "Distance‐directed augmenting path algorithms for maximum flow and parametric maximum flow problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 38(3), pages 413-430, June.
    4. Castro, Jordi, 2006. "Minimum-distance controlled perturbation methods for large-scale tabular data protection," European Journal of Operational Research, Elsevier, vol. 171(1), pages 39-52, May.
    5. Minoux, M., 1984. "A polynomial algorithm for minimum quadratic cost flow problems," European Journal of Operational Research, Elsevier, vol. 18(3), pages 377-387, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. A. Sedeño-Noda & M. González-Sierra & C. González-Martín, 2000. "An algorithmic study of the Maximum Flow problem: A comparative statistical analysis," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(1), pages 135-162, June.
    2. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    3. Castro, Jordi, 2012. "Recent advances in optimization techniques for statistical tabular data protection," European Journal of Operational Research, Elsevier, vol. 216(2), pages 257-269.
    4. Ahuja, Ravindra K., 1956- & Kanpur, I. I. T. & Orlin, James B., 1953-, 1993. "A capacity scaling algorithm for the constrained maximum flow problem," Working papers 3587-93., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Jan Pablo Burgard & Ralf Münnich, 2015. "Sae Teaching Using Simulations," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 603-610, December.
    6. Johannes Gräb & Michael Grimm, 2008. "Spatial inequalities explained - Evidence from Burkina Faso," Ibero America Institute for Econ. Research (IAI) Discussion Papers 173, Ibero-America Institute for Economic Research.
    7. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
    8. Miguel Boubeta & María José Lombardía & Domingo Morales, 2016. "Empirical best prediction under area-level Poisson mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 548-569, September.
    9. Lixia Diao & David D. Smith & Gauri Sankar Datta & Tapabrata Maiti & Jean D. Opsomer, 2014. "Accurate Confidence Interval Estimation of Small Area Parameters Under the Fay–Herriot Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 497-515, June.
    10. Niu, Yi-Feng & Gao, Zi-You & Lam, William H.K., 2017. "A new efficient algorithm for finding all d-minimal cuts in multi-state networks," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 151-163.
    11. Han Ying, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 30-34, August.
    12. Federica Ricca & Andrea Scozzari & Paolo Serafini & Bruno Simeone, 2012. "Error minimization methods in biproportional apportionment," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 547-577, October.
    13. M. D. Ugarte & A. F. Militino & T. Goicoa, 2008. "Adjusting economic estimates in business surveys," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(11), pages 1253-1265.
    14. Ying Han, 2020. "Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 30-34, August.
    15. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.
    16. Ralf Münnich & Jan Pablo Burgard, 2015. "SAE teaching using simulations," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 603-610, December.
    17. Saeideh Kamgar & Florian Meinfelder & Ralf Münnich & Hamidreza Navvabpour, 2020. "Estimation within the new integrated system of household surveys in Germany," Statistical Papers, Springer, vol. 61(5), pages 2091-2117, October.
    18. José Lombardía, María & Sperlich, Stefan, 2012. "A new class of semi-mixed effects models and its application in small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2903-2917.
    19. Patriksson, Michael, 2008. "A survey on the continuous nonlinear resource allocation problem," European Journal of Operational Research, Elsevier, vol. 185(1), pages 1-46, February.
    20. Sun, Hanmei & Jiang, Jiming & Nguyen, Thuan & Luan, Yihui, 2018. "Best look-alike prediction: Another look at the Bayesian classifier and beyond," Statistics & Probability Letters, Elsevier, vol. 143(C), pages 37-42.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:196:y:2009:i:1:p:360-369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.