Nonlinear variable selection with continuous outcome: a fully nonparametric incremental forward stagewise approach.

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Statistical Analysis and Data Mining Pub Date : 2018-08-01 Epub Date: 2018-06-19 DOI:10.1002/sam.11381

Tianwei Yu

引用次数: 0

Abstract

We present a method of variable selection for the sparse generalized additive model. The method doesn't assume any specific functional form, and can select from a large number of candidates. It takes the form of incremental forward stagewise regression. Given no functional form is assumed, we devised an approach termed "roughening" to adjust the residuals in the iterations. In simulations, we show the new method is competitive against popular machine learning approaches. We also demonstrate its performance using some real datasets. The method is available as a part of the nlnet package on CRAN (https://cran.r-project.org/package=nlnet).

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具有连续结果的非线性变量选择:一种完全非参数渐进式前向阶段方法。

提出了一种稀疏广义加性模型的变量选择方法。该方法不假设任何特定的函数形式，并且可以从大量候选项中进行选择。它采用增量前向阶段回归的形式。假设没有功能形式，我们设计了一种称为“粗糙化”的方法来调整迭代中的残差。在模拟中，我们证明了新方法与流行的机器学习方法具有竞争力。我们还用一些真实的数据集证明了它的性能。该方法作为nlnet包的一部分在CRAN (https://cran.r-project.org/package=nlnet)上可用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.