Functional sufficient dimension reduction through information maximization with application to classification

IF 1.2 4区数学 Q2 STATISTICS & PROBABILITY

Journal of Applied Statistics Pub Date : 2024-04-03 DOI:10.1080/02664763.2024.2335570

Xinyu Li, Jianjun Xu, Haoyang Cheng

引用次数: 0

Abstract

Considering the case where the response variable is a categorical variable and the predictor is a random function, two novel functional sufficient dimensional reduction (FSDR) methods are proposed ...

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通过信息最大化实现功能充分降维并应用于分类

考虑到响应变量为分类变量、预测变量为随机函数的情况，提出了两种新颖的函数充分降维（FSDR）方法 ...

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

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.