多元统计方法的改进与应用综述

Q4 Mathematics

Model Assisted Statistics and Applications Pub Date : 2022-05-23 DOI:10.3233/mas-220017

S. Lipovetsky

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引用次数: 2

摘要

这项工作考虑了各种多元统计技术在他们的修改和应用管理，信息系统，经济学，决策和市场研究问题。方法包括多路矩阵特征向量分析、对偶偏最小二乘分析、修正因子和聚类分析、增强典型相关分析等。这些方法已经在许多实际项目中得到应用，并被证明对数据分析师、管理人员和决策者在解决实际问题时非常有用。

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Multivariate statistical methods: A brief review on their modifications and applications

The work considers various multivariate statistical techniques in their modifications and applications to management, information systems, economics, decision making, and marketing research problems. The methods include eigenvectors for many-way matrices, dual partial lest squares, modified factor and cluster analyses, and enhanced canonical correlation analysis. These approaches have been applied in numerous real projects and proved to be useful for data analysts, managers, and decision makers in solving practical problems.

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来源期刊

Model Assisted Statistics and Applications Mathematics-Applied Mathematics

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.