IDEAS home Printed from https://ideas.repec.org/a/spr/sankhb/v83y2021i1d10.1007_s13571-019-00215-9.html
   My bibliography  Save this article

Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling

Author

Listed:
  • Martin Klein

    (U.S. Food and Drug Administration (FDA), CDER/OTS/OB/DBVIII)

  • Ricardo Moura

    (Portuguese Navy Research Center (CINAV)
    Center for Mathematics and Aplications (CMA/FCT/UNL))

  • Bimal Sinha

    (University of Maryland, Baltimore County
    U.S. Census Bureau)

Abstract

In this paper we consider singly imputed synthetic data generated via plug-in sampling under the multivariate normal model. Based on the observed synthetic dataset, we derive a statistical test for the generalized variance, the sphericity test, a test for independence between two subsets of variables, and a test for the regression of one set of variables on the other. The procedures are based on finite sample theory.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-019-00215-9
    DOI: 10.1007/s13571-019-00215-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13571-019-00215-9
    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/s13571-019-00215-9?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," International Statistical Review, International Statistical Institute, vol. 79(3), pages 362-384, December.
    2. Klein, Martin & Sinha, Bimal, 2015. "Likelihood-based inference for singly and multiply imputed synthetic data under a normal model," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 168-175.
    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. 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.
    2. 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).
    3. Biswajit Basak & Bimal Sinha, 2024. "Analysis of One-Way ANOVA Model using Synthetic Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 164-190, May.
    4. 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.
    5. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.
    6. 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.
    7. Ron S. Jarmin & John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Nathan Goldschlag & Michael B. Hawes & Sallie Ann Keller & Daniel Kifer & Philip Leclerc & Jerome P. Reiter & Rolando A. Rodrígue, 2023. "An in-depth examination of requirements for disclosure risk assessment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(43), pages 2220558120-, October.
    8. David R. Munro, 2021. "Consumer Behavior and Firm Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 845-873, June.
    9. Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October.
    10. Dong Hua & Meeden Glen, 2016. "Constructing Synthetic Samples," Journal of Official Statistics, Sciendo, vol. 32(1), pages 113-127, March.
    11. Ori Heffetz & Katrina Ligett, 2014. "Privacy and Data-Based Research," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 75-98, Spring.
    12. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    13. 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.
    14. Gary Benedetto & Jordan C. Stanley & Evan Totty, 2018. "The Creation and Use of the SIPP Synthetic Beta v7.0," CES Technical Notes Series 18-03, Center for Economic Studies, U.S. Census Bureau.
    15. 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.
    16. 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.
    17. Hang J. Kim & Jörg Drechsler & Katherine J. Thompson, 2021. "Synthetic microdata for establishment surveys under informative sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 255-281, January.
    18. 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.
    19. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-019-00215-9. 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.