IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v32y2016i1p113-127n5.html
   My bibliography  Save this article

Constructing Synthetic Samples

Author

Listed:
  • Dong Hua

    (Gilead Sciences, Inc. Foster City, CA 94404, U.S.A.)

  • Meeden Glen

    (School of Statistics, University of Minnesota, Minneapolis, MN 55455, U.S.A.)

Abstract

We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of some of its variables are known. In addition, we assume that we have in hand samples from two similar populations. Using the known population means, we will select subsamples from the samples of the other two populations which we will then combine to construct the synthetic sample. The synthetic sample is obtained by solving an optimization problem, where the known population means, are used as constraints. The optimization is achieved through an adaptive random search algorithm. Simulation studies are presented to demonstrate the effectiveness of our approach. We observe that on average, such synthetic samples behave very much like actual samples from the population of interest. As an application we consider constructing a one-percent synthetic sample for the missing 1890 decennial sample of the United States.

Suggested Citation

  • Dong Hua & Meeden Glen, 2016. "Constructing Synthetic Samples," Journal of Official Statistics, Sciendo, vol. 32(1), pages 113-127, March.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:1:p:113-127:n:5
    DOI: 10.1515/jos-2016-0005
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jos-2016-0005
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jos-2016-0005?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. Michael A. Hidiroglou & Normand Laniel, 2001. "Sampling and Estimation Issues for Annual and Sub‐annual Canadian Business Surveys," International Statistical Review, International Statistical Institute, vol. 69(3), pages 487-504, December.
    2. Christine N. Kohnen & Jerome P. Reiter, 2009. "Multiple imputation for combining confidential data owned by two agencies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 511-528, April.
    3. Jerome P. Reiter, 2005. "Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 185-205, 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. Jerome P. Reiter, 2009. "Using Multiple Imputation to Integrate and Disseminate Confidential Microdata," International Statistical Review, International Statistical Institute, vol. 77(2), pages 179-195, August.
    2. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    3. Klein Martin & Sinha Bimal, 2013. "Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation," Journal of Official Statistics, Sciendo, vol. 29(3), pages 425-465, June.
    4. Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October.
    5. Merz, Joachim, 2002. "Zur Kumulation von Haushaltsstichproben," MPRA Paper 5991, University Library of Munich, Germany.
    6. Martin Klein & Ricardo Moura & Bimal Sinha, 2021. "Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 273-287, May.
    7. Joseph W. Sakshaug & Trivellore E. Raghunathan, 2014. "Generating synthetic microdata to estimate small area statistics in the American Community Survey," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 341-368, June.
    8. Reiter, Jerome P., 2008. "Selecting the number of imputed datasets when using multiple imputation for missing data and disclosure limitation," Statistics & Probability Letters, Elsevier, vol. 78(1), pages 15-20, January.
    9. Joshua Snoke & Gillian M. Raab & Beata Nowok & Chris Dibben & Aleksandra Slavkovic, 2018. "General and specific utility measures for synthetic data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 663-688, June.
    10. Andrés F. Barrientos & Alexander Bolton & Tom Balmat & Jerome P. Reiter & John M. de Figueiredo & Ashwin Machanavajjhala & Yan Chen & Charles Kneifel & Mark DeLong, 2017. "A Framework for Sharing Confidential Research Data, Applied to Investigating Differential Pay by Race in the U. S. Government," NBER Working Papers 23534, National Bureau of Economic Research, Inc.
    11. Yi Qian & Hui Xie, 2013. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," NBER Working Papers 19586, National Bureau of Economic Research, Inc.
    12. Sonya Vartivarian & John L. Czajka & Michael Weber, "undated". "Measuring Disclosure Risk and an Examination of the Possibilities of Using Synthetic Data in the Individual Income Tax Return Public Use File," Mathematica Policy Research Reports ab85aed60a3e429786cfcbfdc, Mathematica Policy Research.
    13. repec:mpr:mprres:5634 is not listed on IDEAS
    14. Joseph W. Sakshaug & Trivellore E. Raghunathan, 2014. "Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(10), pages 2103-2122, October.
    15. Harrison Quick & Scott H. Holan & Christopher K. Wikle, 2018. "Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 649-661, June.
    16. Goldstein Harvey & Shlomo Natalie, 2020. "A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets," Journal of Official Statistics, Sciendo, vol. 36(1), pages 89-115, March.
    17. Whittaker, Gerald & Färe, Rolf & Grosskopf, Shawna & Barnhart, Bradley & Bostian, Moriah & Mueller-Warrant, George & Griffith, Stephen, 2017. "Spatial targeting of agri-environmental policy using bilevel evolutionary optimization," Omega, Elsevier, vol. 66(PA), pages 15-27.
    18. Nigel Melville & Michael McQuaid, 2012. "Research Note ---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation," Information Systems Research, INFORMS, vol. 23(2), pages 559-574, June.
    19. Loong Bronwyn & Rubin Donald B., 2017. "Multiply-Imputed Synthetic Data: Advice to the Imputer," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1005-1019, December.
    20. Chu, Amanda M.Y. & Ip, Chun Yin & Lam, Benson S.Y. & So, Mike K.P., 2022. "Vine copula statistical disclosure control for mixed-type data," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    21. Myron Gutmann & Kristine Witkowski & Corey Colyer & JoAnne O’Rourke & James McNally, 2008. "Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 27(6), pages 639-665, December.

    More about this item

    Statistics

    Access and download statistics

    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:32:y:2016:i:1:p:113-127:n:5. 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.