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Order selection tests with multiply imputed data

  • Consentino, Fabrizio
  • Claeskens, Gerda
Registered author(s):

    Nonparametric tests for the null hypothesis that a function has a prescribed form are developed and applied to data sets with missing observations. Omnibus nonparametric tests such as the order selection tests, do not need to specify a particular alternative parametric form, and have power against a large range of alternatives. More specifically, likelihood-based order selection tests are defined that can be used for multiply imputed data when the data are missing-at-random. A simulation study and data analysis illustrate the performance of the tests. In addition, an Akaike information criterion for model selection is presented that can be used with multiply imputed datasets.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4YWYYM9-3/2/908c6dcf86d0e106c46c4215d9c2ea12
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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 54 (2010)
    Issue (Month): 10 (October)
    Pages: 2284-2295

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    Handle: RePEc:eee:csdana:v:54:y:2010:i:10:p:2284-2295
    Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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    1. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
    2. Jerome P. Reiter, 2007. "Small-sample degrees of freedom for multi-component significance tests with multiple imputation for missing data," Biometrika, Biometrika Trust, vol. 94(2), pages 502-508.
    3. Xiaowei Yang & Thomas R. Belin & W. John Boscardin, 2005. "Imputation and Variable Selection in Linear Regression Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 61(2), pages 498-506, 06.
    4. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
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