Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity

Y. Andriyana, I. Gijbels () and A. Verhasselt
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Y. Andriyana: KU Leuven
I. Gijbels: KU Leuven
A. Verhasselt: Universiteit Hasselt

Statistical Papers, 2018, vol. 59, issue 4, No 20, 1589-1621

Abstract: Abstract Quantile regression is an important tool for describing the characteristics of conditional distributions. Population conditional quantile functions cannot cross for different quantile orders. Unfortunately estimated regression quantile curves often violate this and cross each other, which can be very annoying for interpretations and further analysis. In this paper we are concerned with flexible varying-coefficient modelling, and develop methods for quantile regression that ensure that the estimated quantile curves do not cross. A second aim of the paper is to allow for some heteroscedasticity in the error modelling, and to also estimate the associated variability function. We investigate the finite-sample performances of the discussed methods via simulation studies. Some applications to real data illustrate the use of the methods in practical settings.

Keywords: B-splines; Crossing quantile curves; Longitudinal data; P-splines; Quantile regression; Quantile sheet; Variability; Varying-coefficient models; 62Gxx; 62Jxx; 62Hxx (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-016-0847-7

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