期刊名称：Journal of Management Science and Engineering
印刷版ISSN：2096-2320
出版年度：2020
卷号：5
期号：2
页码：105-124
DOI：10.1016/j.jmse.2019.11.001
语种：English
出版社：Elsevier
摘要：AbstractThe increasing richness of data encourages a comprehensive understanding of economic and financial activities, where variables of interest may include not only scalar (point-like) indicators, but also functional (curve-like) and compositional (pie-like) ones. In many research topics, the variables are also chronologically collected across individuals, which falls into the paradigm of longitudinal analysis. The complicated nature of data, however, increases the difficulty of modeling these variables under the classic longitudinal framework. In this study, we investigate the linear mixed-effects model (LMM) for such complex data. Different types of variables are first consistently represented using the corresponding basis expansions so that the classic LMM can then be conducted on them, which generalizes the theoretical framework of LMM to complex data analysis. A number of simulation studies indicate the feasibility and effectiveness of the proposed model. We further illustrate its practical utility in a real data study on Chinese stock market and show that the proposed method can enhance the performance and interpretability of the regression for complex data with diversified characteristics.
关键词：Longitudinal complex data;Linear mixed-effects model;Compositional data analysis;Functional data analysis;Chinese stock market;Online investors' sentiment
