0600155 20250317092607.920241102235959.9 10.1007/978-3-031-61853-6_12 10 s. 618 P cav_un_epca*0602332978-3-031-61852-9231-240ChamSpringer2024BarigozziMatteoHörmannSiegfriedPaindaveineDavy Regression quantile R-estimator Brownian Bridge cav_un_auth*0368969 Jurečková Jana UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI CZ 100 K Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2024/SI/jureckova-0600155.pdf https://link.springer.com/chapter/10.1007/978-3-031-61853-6_12 GA22-03636S GA ČR CZ cav_un_auth*0435411 We consider the linear regression model, along with the process induced by its α-regression quantile, 0 <α< 1. While only the intercept component of the α-regression quantile estimates the quantile F^−1(α) of the model errors, the α also affects the slope components, whose dispersion infinitely increases as α → 0, 1, in the same rate as the variance of the sample α-quantile. The process of the slope components of α-regression quantile over α ∈ (0, 1) is asymptotically equivalent to the process of R-estimates of the slope parameters in the linear model, generated by the Hájek rank scores. Both processes converge to the vector of independent Brownian bridges under exponentially tailed parent distribution F, after standardization by f (F^−1(α)). BB 10000 10100 10103 2025 JINE 1 RVO:67985556 https://hdl.handle.net/11104/0357780 S A 2024 M SCOPUS PUBMED WOS cav_un_epca*0602332 Recent Advances in Econometrics and Statistics Springer 2024 Cham 231 240 978-3-031-61852-9 Barigozzi Matteo 340 Hörmann Siegfried 340 Paindaveine Davy 340