Implicitly weighted robust estimation of quantiles in linear regression

0509648 - ÚI 2020 RIV CZ eng C - Conference Paper (international conference)
Kalina, Jan - Vidnerová, Petra
Implicitly weighted robust estimation of quantiles in linear regression.
Conference Proceedings. 37th International Conference on Mathematical Methods in Economics 2019. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Economics, 2019 - (Houda, M.; Remeš, R.), s. 25-30. ISBN 978-80-7394-760-6.
[MME 2019: International Conference on Mathematical Methods in Economics /37./. České Budějovice (CZ), 11.09.2019-13.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S; GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : regression quantiles * robust regression * outliers * leverage points
OECD category: Statistics and probability
https://mme2019.ef.jcu.cz/files/conference_proceedings.pdf

Estimation of quantiles represents a very important task in econometric regression modeling, while the standard regression quantiles machinery is well developed as well as popular with a large number of econometric applications. Although regression quantiles are commonly known as robust tools, they are vulnerable to the presence of leverage points in the data. We propose here a novel approach for the linear regression based on a specific version of the least weighted squares estimator, together with an additional estimator based only on observations between two different novel quantiles. The new methods are conceptually simple and comprehensible. Without the ambition to derive theoretical properties of the novel methods, numerical computations reveal them to perform comparably to standard regression quantiles, if the data are not contaminated by outliers. Moreover, the new methods seem much more robust on a simulated dataset with severe leverage points.
Permanent Link: http://hdl.handle.net/11104/0300322