On nonparametric classification with missing covariates
General procedures are proposed for nonparametric classification in the presence of missing covariates. Both kernel-based imputation as well as Horvitz-Thompson-type inverse weighting approaches are employed to handle the presence of missing covariates. In the case of imputation, it is a certain regression function which is being imputed (and not the missing values). Using the theory of empirical processes, the performance of the resulting classifiers is assessed by obtaining exponential bounds on the deviations of their conditional errors from that of the Bayes classifier. These bounds, in conjunction with the Borel-Cantelli lemma, immediately provide various strong consistency results.
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Volume (Year): 98 (2007)
Issue (Month): 5 (May)
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Wang Q. & Linton O. & Hardle W., 2004.
"Semiparametric Regression Analysis With Missing Response at Random,"
Journal of the American Statistical Association,
American Statistical Association, vol. 99, pages 334-345, January.
- Wolfgang Hardle & Oliver Linton & Qihua Wang, 2003. "Semiparametric regression analysis with missing response at random," CeMMAP working papers CWP11/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003.
"Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,"
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- Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
- Hazelton, Martin L., 2000. "Marginal density estimation from incomplete bivariate data," Statistics & Probability Letters, Elsevier, vol. 47(1), pages 75-84, March.
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