通过优化模型平均改进张量回归

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-09-13 DOI:10.1080/01621459.2024.2398164

Qiushi Bu, Hua Liang, Xinyu Zhang, Jiahui Zou

引用次数: 0

摘要

张量在神经成像、数据挖掘、数字营销等领域有着广泛的应用。CANDECOMP/PARAFAC（CP）张量分解能有效减少参数数量，从而获得更高的维度。

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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Improving tensor regression by optimal model averaging

Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensional...

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.