FAStEN：高维函数回归中特征选择和估计的高效自适应方法

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-09-27 DOI:10.1080/10618600.2024.2407464

Tobia Boschi, Lorenzo Testa, Francesca Chiaromonte, Matthew Reimherr

引用次数: 0

摘要

函数回归分析是当代许多科学应用的既定工具。涉及大型复杂数据集的回归问题无处不在，而特征选择 ...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions

Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection ...

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.