IDEAS home Printed from https://ideas.repec.org/a/spr/alstar/v93y2009i2p175-186.html
   My bibliography  Save this article

Semiparametric predictive mean matching

Author

Listed:
  • Marco Di Zio
  • Ugo Guarnera

Abstract

No abstract is available for this item.

Suggested Citation

  • Marco Di Zio & Ugo Guarnera, 2009. "Semiparametric predictive mean matching," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 93(2), pages 175-186, June.
  • Handle: RePEc:spr:alstar:v:93:y:2009:i:2:p:175-186
    DOI: 10.1007/s10182-008-0081-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10182-008-0081-2
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10182-008-0081-2?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. Hunt, Lynette & Jorgensen, Murray, 2003. "Mixture model clustering for mixed data with missing information," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 429-440, January.
    2. 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.
    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. Stefano Boscolo, 2019. "Quantifying the Redistributive Effect of the Erosion of the Italian Personal Income Tax Base: A Microsimulation Exercise," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2019(2), pages 39-80.

    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. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
    2. 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.
    3. 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.
    4. Hendrik Jürges & Lars Thiel & Tabea Bucher-Koenen & Johannes Rausch & Morten Schuth & Axel Börsch-Supan, 2014. "Health, Financial Incentives, and Early Retirement: Microsimulation Evidence for Germany," NBER Chapters, in: Social Security Programs and Retirement Around the World: Disability Insurance Programs and Retirement, pages 285-330, National Bureau of Economic Research, Inc.
    5. 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.
    6. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," RatSWD Working Papers 165, German Data Forum (RatSWD).
    7. 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.
    8. Dang, Hai-Anh & 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).
    9. Di Zio, Marco & Guarnera, Ugo & Luzi, Orietta, 2007. "Imputation through finite Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5305-5316, July.
    10. 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.
    11. 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.
    12. 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.
    13. Eric French & John Bailey Jones, 2011. "The Effects of Health Insurance and Self‐Insurance on Retirement Behavior," Econometrica, Econometric Society, vol. 79(3), pages 693-732, May.
    14. 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.
    15. 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.
    16. Luci Ellis & Jeremy Lawson & Laura Roberts-Thomson, 2003. "Housing Leverage in Australia," RBA Research Discussion Papers rdp2003-09, Reserve Bank of Australia.
    17. 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.
    18. 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.
    19. Christopher R. Bollinger & Barry T. Hirsch, 2013. "Is Earnings Nonresponse Ignorable?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 407-416, May.
    20. Joachim R. Frick & Markus M. Grabka, 2007. "Item Non-response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," Discussion Papers of DIW Berlin 736, DIW Berlin, German Institute for Economic Research.

    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:alstar:v:93:y:2009:i:2:p:175-186. 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.