The Process Induced by Slope Components of α-Regression Quantile

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00600155" target="_blank" >RIV/67985556:_____/24:00600155 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-61853-6_12" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-61853-6_12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-61853-6_12" target="_blank" >10.1007/978-3-031-61853-6_12</a>

Result language
angličtina
Original language name
The Process Induced by Slope Components of α-Regression Quantile
Original language description
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 nequivalent 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)).
Czech name
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Czech description
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Project
<a href="/en/project/GA22-03636S" target="_blank" >GA22-03636S: Aggregation of Methodologies Based on Economic Data</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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