IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v29y2013i3p375-396n7.html
   My bibliography  Save this article

Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates

Author

Listed:
  • Williams Matthew

    (Research and Development Division, National Agricultural Statistics Service, U. S. Department of Agriculture,Fairfax, VA 22030, U.S.A.)

  • Berg Emily

    (Department of Statistics, Iowa State University, Ames, IA 50011, U.S.A.)

Abstract

We examine the incorporation of analyst input into the constrained estimation process. In the calibration literature, there are numerous examples of estimators with “optimal” properties. We show that many of these can be derived from first principles. Furthermore, we provide mechanisms for injecting user input to create user-constrained optimal estimates. We include derivations for common deviance measures with linear and nonlinear constraints and we demonstrate these methods on a contingency table and a simulated survey data set. R code and examples are available at https://github.com/mwilli/Constrained-estimation.git.

Suggested Citation

  • Williams Matthew & Berg Emily, 2013. "Incorporating User Input Into Optimal Constraining Procedures for Survey Estimates," Journal of Official Statistics, Sciendo, vol. 29(3), pages 375-396, June.
    • Handle: RePEc:vrs:offsta:v:29:y:2013:i:3:p:375-396:n:7
    DOI: 10.2478/jos-2013-0032
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/jos-2013-0032
    Download Restriction: no
    File URL: https://libkey.io/10.2478/jos-2013-0032?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
    ---><---

    References listed on IDEAS

    as
    1. Adrian Pizzinga, 2010. "Constrained Kalman Filtering: Additional Results," International Statistical Review, International Statistical Institute, vol. 78(2), pages 189-208, August.
    2. J. Chen, 2002. "Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys," Biometrika, Biometrika Trust, vol. 89(1), pages 230-237, March.
    3. D'Arrigo, Julia & Skinner, Chris J., 2010. "Linearization variance estimation for generalized raking estimators in the presence of nonresponse," LSE Research Online Documents on Economics 39120, London School of Economics and Political Science, LSE Library.
    4. Ted Chang & Phillip S. Kott, 2008. "Using calibration weighting to adjust for nonresponse under a plausible model," Biometrika, Biometrika Trust, vol. 95(3), pages 555-571.
    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. Denis Devaud & Yves Tillé, 2019. "Deville and Särndal’s calibration: revisiting a 25-years-old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1033-1065, December.
    2. Kott Phillip S., 2013. "Discussion," Journal of Official Statistics, Sciendo, vol. 29(3), pages 359-362, June.
    3. Carl-Erik Särndal & Imbi Traat & Kaur Lumiste, 2018. "Interaction Between Data Collection And Estimation Phases In Surveys With Nonresponse," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 183-200, June.
    4. Variyath A. M., 2013. "Empirical Likelihood Based Control Charts," Stochastics and Quality Control, De Gruyter, vol. 28(1), pages 37-44, October.
    5. Christopher L. Foote & Tyler Hounshell & William D. Nordhaus & Douglas Rivers & Pamela Torola, 2021. "Measuring the US Employment Situation Using Online Panels: The Yale Labor Survey," Current Policy Perspectives 93422, Federal Reserve Bank of Boston.
    6. Maciej Berȩsewicz & Dagmara Nikulin, 2021. "Estimation of the size of informal employment based on administrative records with non‐ignorable selection mechanism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 667-690, June.
    7. Xiaogang Duan & Guosheng Yin, 2017. "Ensemble Approaches to Estimating the Population Mean with Missing Response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 899-917, December.
    8. Jiang, Depeng & Zhao, Puying & Tang, Niansheng, 2016. "A propensity score adjustment method for regression models with nonignorable missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 98-119.
    9. Jae Kwang Kim & Mingue Park, 2010. "Calibration Estimation in Survey Sampling," International Statistical Review, International Statistical Institute, vol. 78(1), pages 21-39, April.
    10. Kajal Dihidar, 2014. "Estimating population mean with missing data in unequal probability sampling," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 369-388, June.
    11. Alexandra Filindra & Melanie Kolbe, 2022. "Latinx identification with whiteness: What drives it, and what effects does it have on political preferences?," Social Science Quarterly, Southwestern Social Science Association, vol. 103(6), pages 1424-1439, November.
    12. Kott Phillip S. & Liao Dan, 2018. "Calibration Weighting for Nonresponse with Proxy Frame Variables (So that Unit Nonresponse Can Be Not Missing at Random)," Journal of Official Statistics, Sciendo, vol. 34(1), pages 107-120, March.
    13. Li, Mengyan & Ma, Yanyuan & Zhao, Jiwei, 2022. "Efficient estimation in a partially specified nonignorable propensity score model," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    14. Nicholas-James Clavet & Jean-Yves Duclos & Bernard Fortin & Steeve Marchand, 2012. "Le Québec, 2004-2030 : une analyse de micro-simulation," CIRANO Project Reports 2012rp-16, CIRANO.
    15. Jan Pablo Burgard & Ralf Münnich & Martin Rupp, 2019. "A Generalized Calibration Approach Ensuring Coherent Estimates with Small Area Constraints," Research Papers in Economics 2019-10, University of Trier, Department of Economics.
    16. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    17. M. Giovanna Ranalli & Alina Matei & Andrea Neri, 2023. "Generalised calibration with latent variables for the treatment of unit nonresponse in sample surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 169-195, March.
    18. Rong Tang & Yun Yang, 2022. "Bayesian inference for risk minimization via exponentially tilted empirical likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1257-1286, September.
    19. Anne Konrad & Jan Pablo Burgard & Ralf Münnich, 2021. "A Two‐level GREG Estimator for Consistent Estimation in Household Surveys," International Statistical Review, International Statistical Institute, vol. 89(3), pages 635-656, December.
    20. Changbao Wu & Shixiao Zhang, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1082-1086, 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:vrs:offsta:v:29:y:2013:i:3:p:375-396:n:7. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.