Original title: Implicitly weighted robust estimation of quantiles in linear regression
Authors: Kalina, Jan ; Vidnerová, Petra
Document type: Papers
Conference/Event: MME 2019: International Conference on Mathematical Methods in Economics /37./, České Budějovice (CZ), 20190911
Year: 2019
Language: eng
Abstract: 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.
Keywords: leverage points; outliers; regression quantiles; robust regression
Project no.: GA19-05704S (CEP), GA18-23827S (CEP)
Funding provider: GA ČR, GA ČR
Host item entry: Conference Proceedings. 37th International Conference on Mathematical Methods in Economics 2019, ISBN 978-80-7394-760-6

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://mme2019.ef.jcu.cz/files/conference_proceedings.pdf
Original record: http://hdl.handle.net/11104/0300322

Permalink: http://www.nusl.cz/ntk/nusl-403665


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