Smooth quantile regression and distributed inference for non-randomly stored big data
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In recent years, many distributed algorithms towards big data quantile regression have been proposed. However, they all rely on the data are stored in random manner. This is seldom in practice, and the violation of this assumption can seriously degrade their performance. Moreover, the non-smooth quantile loss brings inconvenience in both computation and theory. To solve these issues, we first propose a convex and smooth quantile loss, which converges to the quantile loss uniformly. Then a novel pilot sample surrogate smooth quantile loss is constructed, which can realize communication-efficient distributed quantile regression, and overcomes the non-randomly distributed nature of big data. In theory, the estimation consistency and asymptotic normality of the resulting distributed estimator are established. The theoretical results guarantee that the new method is adaptive to the situation where the data are stored in any arbitrary way, and can work well just as all the data were pooled on a single machine. Numerical experiments on both synthetic and real data verify the good performance of the new method.
Açıklama
The research was supported by NNSF project of China (11901356) .
Anahtar Kelimeler
Quantile Regression, Big Data, Communication Efficiency, Median Regression, Selection
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
215
Sayı
Künye
Wang, K., Jia, J., Polat, K., Sun, X., Alhudhaif, A., & Alenezi, F. (2023). Smooth quantile regression and distributed inference for non-randomly stored big data. Expert Systems with Applications, 215, 119418.
