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Nonparametric Quantile Regression Estimation with Mixed Discrete and Continuous Data

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JournalJournal of Business and Economic Statistics
DateAccepted/In press - 18 Feb 2020
DateE-pub ahead of print - 16 Mar 2020
DatePublished (current) - Jun 2021
Issue number3
Number of pages16
Pages (from-to)741–756
Early online date16/03/20
Original languageEnglish


In this paper, we investigate the problem of nonparametrically estimating a conditional quantile function with mixed discrete and continuous covariates. A local linear smoothing technique combining both continuous and discrete kernel functions is introduced to estimate the conditional quantile function. We propose using a fully data-driven cross-validation approach to choose the bandwidths, and further derive the asymptotic optimality theory. In addition, we also establish the asymptotic distribution and uniform consistency (with convergence rates) for the local linear conditional quantile estimators with the data-dependent optimal bandwidths. Simulations show that the proposed approach compares well with some existing methods. Finally, an empirical application with the data taken from the IMDb website is presented to analyze the relationship between box office revenues and online rating scores.

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© 2020 American Statistical Association. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

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