IDEAS home Printed from https://ideas.repec.org/a/spr/sankha/v86y2024i1d10.1007_s13171-024-00348-8.html
   My bibliography  Save this article

Finite Population Survey Sampling: An Unapologetic Bayesian Perspective

Author

Listed:
  • Sudipto Banerjee

    (University of California Los Angeles)

Abstract

This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies. Beginning with an overview of Bayesian hierarchical models, including some that yield design-based Horvitz-Thompson estimators, the article proceeds to introduce dependence in finite populations and sets out inferential frameworks for ignorable and nonignorable responses. Multivariate dependencies using graphical models and spatial processes are discussed and some salient features of two recent analyses for spatial finite populations are presented.

Suggested Citation

  • Sudipto Banerjee, 2024. "Finite Population Survey Sampling: An Unapologetic Bayesian Perspective," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 95-124, November.
  • Handle: RePEc:spr:sankha:v:86:y:2024:i:1:d:10.1007_s13171-024-00348-8
    DOI: 10.1007/s13171-024-00348-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13171-024-00348-8
    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/s13171-024-00348-8?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. Graham Kalton, 2019. "Developments in Survey Research over the Past 60 Years: A Personal Perspective," International Statistical Review, International Statistical Institute, vol. 87(S1), pages 10-30, May.
    2. 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.
    3. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    4. Gong Tang, 2003. "Analysis of multivariate missing data with nonignorable nonresponse," Biometrika, Biometrika Trust, vol. 90(4), pages 747-764, December.
    5. Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
    6. Malay Ghosh, 2012. "Finite population sampling: a model-design synthesis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 235-242, June.
    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. Dipak Dey & Subhashis Ghosal & Tapas Samanta, 2024. "Editorial Article: Remembering D. Basu’s Legacy in Statistics," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 1-7, November.

    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. Lucia Paci & Alan E. Gelfand & and María Asunción Beamonte & Pilar Gargallo & Manuel Salvador, 2020. "Spatial hedonic modelling adjusted for preferential sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 169-192, January.
    2. 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.
    3. André Victor Ribeiro Amaral & Elias Teixeira Krainski & Ruiman Zhong & Paula Moraga, 2024. "Model-Based Geostatistics Under Spatially Varying Preferential Sampling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 766-792, December.
    4. John M. Humphreys & Robert B. Srygley & David H. Branson, 2022. "Geographic Variation in Migratory Grasshopper Recruitment under Projected Climate Change," Geographies, MDPI, vol. 2(1), pages 1-19, January.
    5. Andrew O. Finley & Hans-Erik Andersen & Chad Babcock & Bruce D. Cook & Douglas C. Morton & Sudipto Banerjee, 2024. "Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 695-722, December.
    6. Saeed Hayati & Kenji Fukumizu & Afshin Parvardeh, 2024. "Kernel mean embedding of probability measures and its applications to functional data analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 447-484, June.
    7. Azar, Pablo D. & Micali, Silvio, 2018. "Computational principal agent problems," Theoretical Economics, Econometric Society, vol. 13(2), May.
    8. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    9. Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
    10. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    11. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    12. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    13. R de Fondeville & A C Davison, 2018. "High-dimensional peaks-over-threshold inference," Biometrika, Biometrika Trust, vol. 105(3), pages 575-592.
    14. Armantier, Olivier & Treich, Nicolas, 2013. "Eliciting beliefs: Proper scoring rules, incentives, stakes and hedging," European Economic Review, Elsevier, vol. 62(C), pages 17-40.
    15. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    16. Finn Lindgren, 2015. "Comments on: Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 35-44, March.
    17. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    18. Kelly Trinh & Bo Zhang & Chenghan Hou, 2025. "Macroeconomic real‐time forecasts of univariate models with flexible error structures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 59-78, January.
    19. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2023. "Forecasting with a panel Tobit model," Quantitative Economics, Econometric Society, vol. 14(1), pages 117-159, January.
    20. Warne, Anders, 2023. "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series 2768, European Central Bank.

    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:sankha:v:86:y:2024:i:1:d:10.1007_s13171-024-00348-8. 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.