On weighted and locally polynomial directional quantile regression

0474696 - ÚTIA 2018 RIV DE eng J - Journal Article
Boček, Pavel - Šiman, Miroslav
On weighted and locally polynomial directional quantile regression.
Computational Statistics. Roč. 32, č. 3 (2017), s. 929-946. ISSN 0943-4062. E-ISSN 1613-9658
R&D Projects: GA ČR GA14-07234S
Institutional support: RVO:67985556
Keywords : Quantile regression * Nonparametric regression * Nonparametric regression
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 0.828, year: 2017
http://library.utia.cas.cz/separaty/2017/SI/bocek-0458380.pdf

The article deals with certain quantile regression methods for vector responses. In particular, it describes weighted and locally polynomial extensions to the projectional quantile regression, discusses their properties, addresses their computational side, compares their outcome with recent analogous generalizations of the competing multiple-output directional quantile regression, demonstrates a link between the two competing methodologies, complements the results already available in the literature, illustrates the concepts with a few simulated and insightful examples illustrating some of their features, and shows their application to a real financial data set, namely to Forex 1M exchange rates. The real-data example strongly indicates that the presented methods might have a huge impact on the analysis of multivariate time series consisting of two to four dimensional observations.
Permanent Link: http://hdl.handle.net/11104/0271770