用于变量选择的基于低维差分的部分线性模型中的边际化 LASSO

IF 1.2 4区数学 Q2 STATISTICS & PROBABILITY

Journal of Applied Statistics Pub Date : 2024-07-09 DOI:10.1080/02664763.2024.2372676

M. Norouzirad, R. Moura, M. Arashi, F. J. Marques

引用次数: 0

摘要

当数据中同时存在线性和非线性预测因子时，基于差分的部分线性模型是一种合适的回归模型。然而，当我们想利用差分模型来优化权重时，就需要使用......

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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Marginalized LASSO in the low-dimensional difference-based partially linear model for variable selection

The difference-based partially linear model is an appropriate regression model when both linear and nonlinear predictors are present in the data. However, when we want to optimize the weights using...

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来源期刊

Journal of Applied Statistics 数学-统计学与概率论

CiteScore

3.40

自引率

0.00%

发文量

126

审稿时长

6 months

期刊介绍： Journal of Applied Statistics provides a forum for communication between both applied statisticians and users of applied statistical techniques across a wide range of disciplines. These areas include business, computing, economics, ecology, education, management, medicine, operational research and sociology, but papers from other areas are also considered. The editorial policy is to publish rigorous but clear and accessible papers on applied techniques. Purely theoretical papers are avoided but those on theoretical developments which clearly demonstrate significant applied potential are welcomed. Each paper is submitted to at least two independent referees.