L1 Correlation-Based Penalty in High-Dimensional Quantile Regression
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, we propose a new method called L1 norm correlation based estimation in quantile regression in high-dimensional sparse models where the number of explanatory variables is large, may be larger than the number of observations, however, only some small subset of the predictive variables are important in explaining the dependent variable. Therefore, the importance of new method is that it addresses both grouping affect and variable selection. Monte Carlo simulations confirm that the new method compares well to the other existing regularization methods.
Açıklama
4th International Conference on Big Data and Information Analytics (BigDIA) -- DEC 17-19, 2018 -- HOUSTON, TX
Anahtar Kelimeler
Variable Selection, Lasso Regression, Correlation Based Penalty, Quantile Regression
Kaynak
2018 4th International Conference on Big Data and Information Analytics (Bigdia)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
