IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v89y2015icp158-171.html
   My bibliography  Save this article

Likelihood inference for small area estimation using data cloning

Author

Listed:
  • Torabi, Mahmoud
  • Lele, Subhash R.
  • Prasad, Narasimha G.N.

Abstract

Policy decisions regarding allocation of resources to subgroups in a population, called small areas, are based on reliable predictors of their underlying parameters. However, in sample surveys, the information to estimate reliable predictors is often insufficient at the level of the small areas. Hence, parameters of the subgroups are often predicted based on the coarser scale data. In view of this, there is a growing demand for reliable small area predictors by borrowing information from other areas. These models are commonly based on either linear mixed models (LMMs) or generalized linear mixed models (GLMMs). The frequentist analysis of LMM, a special case of GLMM, is computationally difficult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of LMM and GLMM computationally convenient. Recently developed data cloning method provides a frequentist approach to complex mixed models which is also computationally convenient. Data cloning which yields to maximum likelihood estimation is used to conduct frequentist analysis of small area estimation for Normal and non-Normal responses. It is shown that for the Normal and non-Normal responses, data cloning leads to predictions and prediction intervals of small area parameters that have reasonably good coverage.

Suggested Citation

  • Torabi, Mahmoud & Lele, Subhash R. & Prasad, Narasimha G.N., 2015. "Likelihood inference for small area estimation using data cloning," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 158-171.
  • Handle: RePEc:eee:csdana:v:89:y:2015:i:c:p:158-171
    DOI: 10.1016/j.csda.2015.03.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947315000821
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2015.03.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Esteban, M.D. & Morales, D. & Pérez, A. & Santamaría, L., 2012. "Small area estimation of poverty proportions under area-level time models," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2840-2855.
    3. Hamilton, James D., 1986. "A standard error for the estimated state vector of a state-space model," Journal of Econometrics, Elsevier, vol. 33(3), pages 387-397, December.
    4. Lele, Subhash R. & Nadeem, Khurram & Schmuland, Byron, 2010. "Estimability and Likelihood Inference for Generalized Linear Mixed Models Using Data Cloning," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1617-1625.
    5. A. C. Singh & D. M. Stukel & D. Pfeffermann, 1998. "Bayesian versus frequentist measures of error in small area estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 377-396.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pedro Chaim & Márcio Poletti Laurini, 2022. "Data Cloning Estimation and Identification of a Medium-Scale DSGE Model," Stats, MDPI, vol. 6(1), pages 1-13, December.

    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. Torabi, Mahmoud & Shokoohi, Farhad, 2012. "Likelihood inference in small area estimation by combining time-series and cross-sectional data," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 213-221.
    2. 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.
    3. Mahmoud Torabi, 2012. "Spatial modeling using frequentist approach for disease mapping," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2431-2439, July.
    4. Danny Pfeffermann & Richard Tiller, 2005. "Bootstrap Approximation to Prediction MSE for State–Space Models with Estimated Parameters," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(6), pages 893-916, November.
    5. Torabi, Mahmoud, 2013. "Likelihood inference in generalized linear mixed measurement error models," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 549-557.
    6. Boubeta, Miguel & Lombardía, María José & Morales, Domingo, 2017. "Poisson mixed models for studying the poverty in small areas," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 32-47.
    7. Marhuenda, Yolanda & Morales, Domingo & del Carmen Pardo, María, 2014. "Information criteria for Fay–Herriot model selection," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 268-280.
    8. Torabi, Mahmoud, 2012. "Likelihood inference in generalized linear mixed models with two components of dispersion using data cloning," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4259-4265.
    9. Tamura, Karin Ayumi & Giampaoli, Viviana, 2013. "New prediction method for the mixed logistic model applied in a marketing problem," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 202-216.
    10. Jan Pablo Burgard & Joscha Krause & Domingo Morales, 2022. "A measurement error Rao–Yu model for regional prevalence estimation over time using uncertain data obtained from dependent survey estimates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 204-234, March.
    11. Shonosuke Sugasawa & Tatsuya Kubokawa & J. N. K. Rao, 2018. "Small area estimation via unmatched sampling and linking models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 407-427, June.
    12. Kordos Jan, 2016. "Development of Small Area Estimation in Official Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 105-132, March.
    13. Jan Kordos, 2016. "Development Of Smallarea Estimation In Official Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 105-132, March.
    14. repec:csb:stintr:v:17:y:2016:i:1:p:105-132 is not listed on IDEAS
    15. Victor Bystrov, 2018. "Measuring the Natural Rates of Interest in Germany and Italy," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(4), pages 333-353, December.
    16. 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.
    17. Gabriele Fiorentini & Alessandro Galesi & Gabriel Pérez-Quirós & Enrique Sentana, 2018. "The rise and fall of the natural interest rate," Working Papers 1822, Banco de España.
    18. Benati, Luca, 2007. "Drift and breaks in labor productivity," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2847-2877, August.
    19. Àlex Costa & Albert Satorra & Eva Ventura, 2003. "An empirical evaluation of small area estimators," Economics Working Papers 674, Department of Economics and Business, Universitat Pompeu Fabra, revised Jun 2003.
    20. Jun Ma & Mark E. Wohar, 2013. "An Unobserved Components Model that Yields Business and Medium-Run Cycles," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1351-1373, October.
    21. J. N. K. Rao, 2015. "Inferential issues in model-based small area estimation: some new developments," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 491-510, December.

    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:csdana:v:89:y:2015:i:c:p:158-171. 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/csda .

    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.