Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2021.1978306
A sparse approach for high-dimensional data with heavy-tailed noise
Yafen Ye
Yuanhai Shao
Chunna Li
Sažetak
High-dimensional data have commonly emerged in diverse fields,
such as economics, finance, genetics, medicine, machine learning,
and so on. In this paper, we consider the sparse quantile regression
problem of high-dimensional data with heavy-tailed noise, especially
when the number of regressors is much larger than the sample size.
We bring the spirit of Lp-norm support vector regression into quantile regression and propose a robust Lp-norm support vector quantile regression for high-dimensional data with heavy-tailed noise. The
proposed method achieves robustness against heavy-tailed noise
due to its use of the pinball loss function. Furthermore, Lp-norm
support vector quantile regression ensures that the most representative variables are selected automatically by using a sparse parameter.
We use a simulation study to test the variable selection performance
of Lp-norm support vector quantile regression, where the number of
explanatory variables greatly exceeds the sample size. The simulation
study confirms that Lp-norm support vector quantile regression is
not only robust against heavy-tailed noise but also selects representative variables. We further apply the proposed method to solve the
variable selection problem of index construction, which also confirms
the robustness and sparseness of Lp-norm support vector quantile regression.
Ključne riječi
High-dimensional data; heavy-tailed noise; Lp-norm support vector quantile regression; variable selection
Hrčak ID:
302482
URI
Datum izdavanja:
31.3.2023.
Posjeta: 416 *