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The Application of Partially Functional Linear Regression Model in Health Science
Science Discovery
Volume 8, Issue 6, December 2020, Pages: 134-138
Received: Nov. 2, 2020; Published: Nov. 4, 2020
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Authors
Weiwei Xiao, School of Science, North China University of Technology, Beijing, China
Yixuan Wang, School of Science, North China University of Technology, Beijing, China
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Abstract
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.
Keywords
Functional Data, Partially Functional Linear Regression, Functional Principal Component Analysis, Health Science
To cite this article
Weiwei Xiao, Yixuan Wang, The Application of Partially Functional Linear Regression Model in Health Science, Science Discovery. Vol. 8, No. 6, 2020, pp. 134-138. doi: 10.11648/j.sd.20200806.13
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