@Article{EAJAM-12-1,
author = {Lican, Kang and Yanyan, Liu and Yuan, Luo and Zhu, Chang},
title = {Hard Thresholding Regularised Logistic Regression: Theory and Algorithms},
journal = {East Asian Journal on Applied Mathematics},
year = {2022},
volume = {12},
number = {1},
pages = {35--52},
abstract = {<p style="text-align: justify;">The hard thresholding regularised logistic regression in high dimensions with
larger number of features than samples is considered. The sharp oracle inequality for
the global solution is established. If the target signal is detectable, it is proven that with
a high probability the estimated and true supports coincide. Starting with the KKT condition, we introduce the primal and dual active sets algorithm for fitting and also consider
a sequential version of this algorithm with a warm-start strategy. Simulations and a real
data analysis show that SPDAS outperforms LASSO, MCP and SCAD methods in terms
of computational efficiency, estimation accuracy, support recovery and classification.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.110121.210621},
url = {https://global-sci.com/article/82452/hard-thresholding-regularised-logistic-regression-theory-and-algorithms}
}