Bootstrap based model checks with missing binary response data
AbstractDikta, Kvesic, and Schmidt proposed a model-based resampling scheme to approximate critical values of tests for model checking involving binary response data. Their approach is inapplicable when the binary response variable is not always observed, however. We propose a missingness adjusted marked empirical process under the framework that the missing binary responses are missing at random. We introduce a resampling scheme for the bootstrap and prove its asymptotic validity. We present some numerical comparisons and illustrate our methodology using a real data set.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 83 (2013)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Dikta, Gerhard & Kvesic, Marsel & Schmidt, Christian, 2006. "Bootstrap Approximations in Model Checks for Binary Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 521-530, June.
- W. Stute, 1992. "Strong consistency of the MLE under random censoring," Metrika, Springer, vol. 39(1), pages 257-267, December.
- Koul, Hira L. & Yi, Tingting, 2006. "Goodness-of-fit testing in interval censoring case 1," Statistics & Probability Letters, Elsevier, vol. 76(7), pages 709-718, April.
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