IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v16y2010i3-4p421-438n10.html
   My bibliography  Save this article

MCMC imputation in autologistic model

Author

Listed:
  • Zalewska Marta

    (Department of Environmental Hazards Prevention and Allergology, Medical University of Warsaw, Zwirki i Wigury 61, 02-091 Warszawa, Poland. E-mail:)

  • Niemiro Wojciech

    (Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Chopina 12/18, 87-100 Toruń, Institute of Applied Mathematics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warszawa, Poland. E-mail:)

  • Samoliński Bolesław

    (Department of Environmental Hazards Prevention and Allergology, Medical University of Warsaw, Zwirki i Wigury 61, 02-091 Warszawa, Poland. E-mail:)

Abstract

We consider statistical inference from incomplete sets of binary data. Our approach is based on the autologistic model, which is very flexible and well suited for medical applications. We propose a Bayesian approach, essentially using Monte Carlo techniques. The method developed in this paper is a special version of Gibbs sampler. We repeat intermittently the following two steps. First, missing values are generated from the predictive distribution. Second, unknown parametes are estimated from the completed data. The Monte Carlo method of computing maximum likelihood estimates due to Geyer and Thompson (J. R. Statist. Soc. B 54: 657–699, 1992) is modified to the Bayesian setting and missing data problems. We include results of some small scale simulation experiments. We artificially introduce missing values in a real data set and then use our algorithm to refill missings. The rate of correct imputations is quite satisfactory.

Suggested Citation

  • Zalewska Marta & Niemiro Wojciech & Samoliński Bolesław, 2010. "MCMC imputation in autologistic model," Monte Carlo Methods and Applications, De Gruyter, vol. 16(3-4), pages 421-438, January.
  • Handle: RePEc:bpj:mcmeap:v:16:y:2010:i:3-4:p:421-438:n:10
    DOI: 10.1515/mcma.2010.017
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma.2010.017
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/mcma.2010.017?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, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    2. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    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. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    2. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    3. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    4. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
    5. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    6. Dang, Hai-Anh H & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    7. Daniel Schunk, 2007. "A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey," MEA discussion paper series 07121, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    8. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    9. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    10. F. Di Lascio & Simone Giannerini & Alessandra Reale, 2015. "Exploring copulas for the imputation of complex dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 159-175, March.
    11. Ankita Patnaik & Jeffrey Hemmeter & Arif Mamun, "undated". "Promoting Readiness of Minors with Autism Spectrum Disorder: Evidence from a Randomized Controlled Trial," Mathematica Policy Research Reports a74c93d9bdce40709ad81cdbc, Mathematica Policy Research.
    12. Westermeier, Christian & Grabka, Markus M., 2016. "Longitudinal Wealth Data and Multiple Imputation: An Evaluation Study," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(3), pages 237-252.
    13. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    14. Ahfock, Daniel & Pyne, Saumyadipta & McLachlan, Geoffrey J., 2022. "Statistical file-matching of non-Gaussian data: A game theoretic approach," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    15. Yanqing Sun & Li Qi & Fei Heng & Peter B. Gilbert, 2020. "A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 791-814, August.
    16. Jonathan Hambur & Gianni La Cava, 2018. "Do Interest Rates Affect Business Investment? Evidence from Australian Company-level Data," RBA Research Discussion Papers rdp2018-05, Reserve Bank of Australia.
    17. Arif Mamun & David Wittenburg & Noelle Denny-Brown & Michael Levere & David Mann & Rebecca Coughlin & Sarah Croake & Heather Gordon & Denise Hoffman & Rachel Holzwart & Rosalind Keith & Brittany McGil, "undated". "Promoting Opportunity Demonstration: Interim Evaluation Report," Mathematica Policy Research Reports caa99d38a8b14f968ea3438e5, Mathematica Policy Research.
    18. Miguel Szekely & Nora Lustig & Martin Cumpa & Jose Antonio Mejia, 2004. "Do we know how much poverty there is?," Oxford Development Studies, Taylor & Francis Journals, vol. 32(4), pages 523-558.
    19. Friedrich Schneider, 2017. "Shadow Economies around the World: New Results for 158 Countries over 1991-2015," Economics working papers 2017-10, Department of Economics, Johannes Kepler University Linz, Austria.
    20. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani, 2016. "Dirty spatial econometrics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 177-189, January.

    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:bpj:mcmeap:v:16:y:2010:i:3-4:p:421-438:n:10. 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.degruyter.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.