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The Application of Partially Functional Linear Regression Model in Health Science

机译:部分功能线性回归模型在健康科学中的应用

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With the rapid development of information technology, data information also presents the Characteristics of diversity. Meanwhile more and more datum are presented in the form of functions. Therefore, functional data has become the focus of researchers. Functional data analysis has also proved to be of great value in the fields of biology, medicine and metrology. A partially functional linear regression model is proposed for the regression cases in which the response variables are scalar types and the predictive variables are both variable types and functional types. For the functional predictive variables, we use the functional principal component analysis method to reduce the dimension of the functional data.The least square method is used to calculate the estimate of parameters.With the improvement of people's living standard, people pay more and more attention to health. And an increasing number of people are eager to live a healthy life and keep healthy. Healthy and comfortable sleep has become a topic of increasing concern to researchers. Using data from PhysioNet Databases on activity and sleep in healthy people for this study, we found that the predicted variables in the model could well explain the response variables. The application of partially functional linear model is further extended.
机译:随着信息技术的快速发展,数据信息也提出了多样性的特征。同时越来越多的基准以功能的形式呈现。因此,功能数据已成为研究人员的重点。功能数据分析也被证明是生物学,医学和计量领域的巨大价值。提出了一个部分功能线性回归模型,用于回归例,其中响应变量是标量类型,并且预测变量都是可变类型和功能类型。对于功能性预测变量,我们使用功能主成分分析方法来减少功能数据的尺寸。最小二乘法用于计算参数的估计。在人们的生活水平的改善,人们越来越多地关注健康。越来越多的人渴望过上健康的生活并保持健康。健康舒适的睡眠已成为研究人员越来越多的睡眠。使用来自Physoionet数据库的数据在活动和睡眠中的健康人员中进行本研究,我们发现模型中的预测变量很好地解释了响应变量。部分功能线性模型的应用进一步延长。

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