L1 Correlation-Based Penalty in High-Dimensional Quantile Regression

YÜZBAŞI B., Ahmed S. E., ASAR Y.

4th International Conference on Big Data and Information Analytics (BigDIA), Texas, Amerika Birleşik Devletleri, 17 - 19 Aralık 2018 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Basıldığı Şehir: Texas
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • İnönü Üniversitesi Adresli: Evet

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.