IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v29y2020i1d10.1007_s10260-019-00458-w.html
   My bibliography  Save this article

Nonparametric imputation method for nonresponse in surveys

Author

Listed:
  • Caren Hasler

    (University of Neuchâtel
    University of Toronto Scarborough)

  • Radu V. Craiu

    (University of Toronto)

Abstract

Many imputation methods are based on a statistical model that assumes the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. Misspecification of this function may lead to severe errors in estimation and to misleading conclusions. Imputation techniques can therefore benefit from flexible formulations that can capture a wide range of patterns. We consider the use of smoothing splines within an additive model framework to estimate the functional dependence between the variable of interest and the auxiliary variables. The estimator obtained allows us to build an imputation model in the case of multiple auxiliary variables. The performance of our method is assessed via numerical experiments involving simulated and real data.

Suggested Citation

  • Caren Hasler & Radu V. Craiu, 2020. "Nonparametric imputation method for nonresponse in surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 25-48, March.
  • Handle: RePEc:spr:stmapp:v:29:y:2020:i:1:d:10.1007_s10260-019-00458-w
    DOI: 10.1007/s10260-019-00458-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-019-00458-w
    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/s10260-019-00458-w?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. Qin J. & Leung D. & Shao J., 2002. "Estimation With Survey Data Under Nonignorable Nonresponse or Informative Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 193-200, March.
    2. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    3. Lee, Thomas C. M., 2003. "Smoothing parameter selection for smoothing splines: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 139-148, February.
    4. Simon N. Wood, 2008. "Fast stable direct fitting and smoothness selection for generalized additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 495-518, July.
    5. G. Chauvet & J.-C. Deville & D. Haziza, 2011. "On balanced random imputation in surveys," Biometrika, Biometrika Trust, vol. 98(2), pages 459-471.
    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. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    2. Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1540-1551, April.
    3. Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.
    4. Sylvie Charlot & Riccardo Crescenzi & Antonio Musolesi, 2014. "Augmented and Unconstrained: revisiting the Regional Knowledge Production Function," SEEDS Working Papers 2414, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Aug 2014.
    5. Longhi, C. & Musolesi, A. & Baumont, C., 2013. "Modeling the industrial dynamics of the European metropolitan areas during the process of economic integration: a semiparametric approach," Working Papers 2013-10, Grenoble Applied Economics Laboratory (GAEL).
    6. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
    7. Musolesi Antonio & Mazzanti Massimiliano, 2014. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 521-541, December.
    8. Vijay A. Murik, 2013. "Bond pricing with a surface of zero coupon yields," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(2), pages 497-512, June.
    9. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    10. Massimiliano Mazzanti & Antonio Musolesi, 2013. "Nonlinearity, Heterogeneity and Unobserved Effects in the CO2-income Relation for Advanced Countries," Working Papers 2013.91, Fondazione Eni Enrico Mattei.
    11. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    12. Paolo Veneri, 2018. "Urban spatial structure in OECD cities: Is urban population decentralising or clustering?," Papers in Regional Science, Wiley Blackwell, vol. 97(4), pages 1355-1374, November.
    13. Nadja Klein & Michel Denuit & Stefan Lang & Thomas Kneib, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," Working Papers 2013-24, Faculty of Economics and Statistics, Universität Innsbruck.
    14. Kim, Young-Ju, 2011. "A comparative study of nonparametric estimation in Weibull regression: A penalized likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1884-1896, April.
    15. Rodríguez-Álvarez, María Xosé & Lee, Dae-Jin & Kneib, Thomas & Durbán, María & Eilers, Paul, 2013. "Fast algorithm for smoothing parameter selection in multidimensional generalized P-splines," DES - Working Papers. Statistics and Econometrics. WS ws133026, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," LIDAM Discussion Papers ISBA 2013045, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    17. M.L. Nores & M.P. Díaz, 2016. "Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 810-826, April.
    18. Iñaki Galán & Lorena Simón & Elena Boldo & Cristina Ortiz & Rafael Fernández-Cuenca & Cristina Linares & María José Medrano & Roberto Pastor-Barriuso, 2017. "Changes in hospitalizations for chronic respiratory diseases after two successive smoking bans in Spain," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    19. Malloy, Elizabeth J. & Spiegelman, Donna & Eisen, Ellen A., 2009. "Comparing measures of model selection for penalized splines in Cox models," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2605-2616, May.
    20. Schwemmer, Philipp & Güpner, Franziska & Adler, Sven & Klingbeil, Knut & Garthe, Stefan, 2016. "Modelling small-scale foraging habitat use in breeding Eurasian oystercatchers (Haematopus ostralegus) in relation to prey distribution and environmental predictors," Ecological Modelling, Elsevier, vol. 320(C), pages 322-333.

    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:stmapp:v:29:y:2020:i:1:d:10.1007_s10260-019-00458-w. 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.