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Regression quantiles with errors-in-variables

Author

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  • D. Ioannides
  • Eric Matzner-Løber

Abstract

In a lot of situations, variables are measured with errors. While this problem has been previously studied in the context of kernel regression, no work has been done in quantile regression. To estimate this function, we use deconvolution kernel estimators. We obtain asymptotic results (MSE and normality) for two estimators of conditional quantiles and analyse their finite sample performances via a large simulation study.

Suggested Citation

  • D. Ioannides & Eric Matzner-Løber, 2009. "Regression quantiles with errors-in-variables," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(8), pages 1003-1015.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:8:p:1003-1015
    DOI: 10.1080/10485250903019515
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    References listed on IDEAS

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    1. A. Delaigle & I. Gijbels, 2002. "Estimation of integrated squared density derivatives from a contaminated sample," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 869-886, October.
    2. Fan, Jianqing & Masry, Elias, 1992. "Multivariate regression estimation with errors-in-variables: Asymptotic normality for mixing processes," Journal of Multivariate Analysis, Elsevier, vol. 43(2), pages 237-271, November.
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