Regression quantiles with errors-in-variables

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 norma...

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Bibliographic Details
Published in:Journal of nonparametric statistics 2009-11, Vol.21 (8), p.1003-1015
Main Authors: Ioannides, D. A., Matzner-Løber, Eric
Format: Article
Language:English
Subjects:
conditional quantiles
deconvolution
Error analysis
errors-in-variables
Estimating techniques
measurement errors
nonparametric estimation
Regression analysis
Simulation
Statistical analysis
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Summary: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.
ISSN:1048-5252
1029-0311
DOI:10.1080/10485250903019515