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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23408

Title: Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity
Authors: Andriyana, Yudhie
Gijbels, Irène
Verhasselt, Anneleen
Issue Date: 2016
Citation: STATISTICAL PAPERS, 59 (4), p. 1589-1621
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.
Notes: Gijbels, I (reprint author), Katholieke Univ Leuven, Dept Math, Leuven, Belgium. irene.gijbels@wis.kuleuven.be
URI: http://hdl.handle.net/1942/23408
DOI: 10.1007/s00362-016-0847-7
ISI #: 000450955500020
ISSN: 0932-5026
Category: A1
Type: Journal Contribution
Validation: vabb, 2018
Appears in Collections: Research publications

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