IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v21y2016i3d10.1007_s13253-016-0250-9.html
   My bibliography  Save this article

A Varying Coefficients Model For Estimating Finite Population Totals: A Hierarchical Bayesian Approach

Author

Listed:
  • Ciro Velasco-Cruz

    (Colegio de Postgraduados)

  • Luis Fernando Contreras-Cruz

    (Universidad Autónoma Chapingo)

  • Eric P. Smith

    (Virginia Tech)

  • José E. Rodríguez

    (Universidad de Guanajuato)

Abstract

In some finite sampling situations, there is a primary variable that is sampled, and there are measurements on covariates for the entire population. A Bayesian hierarchical model for estimating totals for finite populations is proposed. A nonparametric linear model is assumed to explain the relationship between the dependent variable of interest and covariates. The regression coefficients in the linear model are allowed to vary as a function of a subset of covariates nonparametrically based on B-splines. The generality of this approach makes it robust and applicable to data collected using a variety of sampling techniques, provided the sample is representative of the finite population. A simulation study is carried out to evaluate the performance of the proposed model for the estimation of the population total. Results indicate accurate estimation of population totals using the approach. The modeling approach is used to estimate the total production of avocado for a large group of groves in Mexico.

Suggested Citation

  • Ciro Velasco-Cruz & Luis Fernando Contreras-Cruz & Eric P. Smith & José E. Rodríguez, 2016. "A Varying Coefficients Model For Estimating Finite Population Totals: A Hierarchical Bayesian Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 548-568, September.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:3:d:10.1007_s13253-016-0250-9
    DOI: 10.1007/s13253-016-0250-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-016-0250-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-016-0250-9?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. Little R.J., 2004. "To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 546-556, January.
    2. Hogan J.W. & Tchernis R., 2004. "Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 314-324, January.
    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. Ivaldi, Enrico, 2013. "Proposal of a country risk index based on a factorial analysis - Una proposta di indice di rischio paese basato sull’analisi fattoriale: una applicazione ai paesi del sud del Mediterraneo e ai paesi d," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 66(2), pages 231-249.
    2. Will Davis & Alexander Gordan & Rusty Tchernis, 2021. "Measuring the spatial distribution of health rankings in the United States," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2921-2936, November.
    3. Little Roderick J., 2013. "Discussion," Journal of Official Statistics, Sciendo, vol. 29(3), pages 363-366, June.
    4. Marta Santagata & Enrico Ivaldi & Riccardo Soliani, 2019. "Development and Governance in the Ex-Soviet Union: An Empirical Inquiry," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 157-190, January.
    5. Kunihama, T. & Herring, A.H. & Halpern, C.T. & Dunson, D.B., 2016. "Nonparametric Bayes modeling with sample survey weights," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 41-48.
    6. Marivoet, Wim & De Herdt, Tom, 2017. "From figures to facts: making sense of socio-economic surveys in the Democratic Republic of the Congo (DRC)," IOB Analyses & Policy Briefs 23, Universiteit Antwerpen, Institute of Development Policy (IOB).
    7. Geoffrey Jones & Wesley O. Johnson, 2016. "A Bayesian Superpopulation Approach to Inference for Finite Populations Based on Imperfect Diagnostic Outcomes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 314-327, June.
    8. J. Andrew Royle, 2009. "Analysis of Capture–Recapture Models with Individual Covariates Using Data Augmentation," Biometrics, The International Biometric Society, vol. 65(1), pages 267-274, March.
    9. Courtemanche, Charles & Soneji, Samir & Tchernis, Rusty, 2013. "Modeling Area-Level Health Rankings," IZA Discussion Papers 7631, Institute of Labor Economics (IZA).
    10. Kim, Hea-Jung & Choi, Taeryon & Jo, Seongil, 2016. "Bayesian factor analysis with uncertain functional constraints about factor loadings," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 110-128.
    11. Peter Congdon, 2011. "The Spatial Pattern of Suicide in the US in Relation to Deprivation, Fragmentation and Rurality," Urban Studies, Urban Studies Journal Limited, vol. 48(10), pages 2101-2122, August.
    12. Congdon, Peter, 2009. "Modelling the impact of socioeconomic structure on spatial health outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3047-3056, June.
    13. Robert Sandy & Gilbert Liu & John Ottensmann & Rusty Tchernis & Jeff Wilson & O. T. Ford, 2011. "Studying the Child Obesity Epidemic with Natural Experiments," NBER Chapters, in: Economic Aspects of Obesity, pages 181-221, National Bureau of Economic Research, Inc.
    14. David Kaplan & Chansoon Lee, 2018. "Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments," Evaluation Review, , vol. 42(4), pages 423-457, August.
    15. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    16. Stephan Stahlschmidt & Wolfgang Karl Härdle & Helmut Thome, 2014. "An Application of Principal Component Analysis on Multivariate Time-Stationary Spatio-Temporal Data," SFB 649 Discussion Papers SFB649DP2014-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    17. Philip A. White & Alan E. Gelfand, 2021. "Multivariate functional data modeling with time-varying clustering," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 586-602, September.
    18. Terrance Savitsky & Daniel McCaffrey, 2014. "Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 275-302, April.
    19. Rubiana Chamarbagwala & Rusty Tchernis, 2006. "The Role of Social Norms in Child Labor and Schooling in India," CAEPR Working Papers 2006-016, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    20. Maura Mezzetti, 2012. "Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 49-74, March.

    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:spr:jagbes:v:21:y:2016:i:3:d:10.1007_s13253-016-0250-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.