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.
| Published in | Science Discovery (Volume 8, Issue 6) |
| DOI | 10.11648/j.sd.20200806.13 |
| Page(s) | 134-138 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Functional Data, Partially Functional Linear Regression, Functional Principal Component Analysis, Health Science
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APA Style
Weiwei Xiao, Yixuan Wang. (2020). The Application of Partially Functional Linear Regression Model in Health Science. Science Discovery, 8(6), 134-138. https://doi.org/10.11648/j.sd.20200806.13
ACS Style
Weiwei Xiao; Yixuan Wang. The Application of Partially Functional Linear Regression Model in Health Science. Sci. Discov. 2020, 8(6), 134-138. doi: 10.11648/j.sd.20200806.13
AMA Style
Weiwei Xiao, Yixuan Wang. The Application of Partially Functional Linear Regression Model in Health Science. Sci Discov. 2020;8(6):134-138. doi: 10.11648/j.sd.20200806.13
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Download@article{10.11648/j.sd.20200806.13,
author = {Weiwei Xiao and Yixuan Wang},
title = {The Application of Partially Functional Linear Regression Model in Health Science},
journal = {Science Discovery},
volume = {8},
number = {6},
pages = {134-138},
doi = {10.11648/j.sd.20200806.13},
url = {https://doi.org/10.11648/j.sd.20200806.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20200806.13},
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.},
year = {2020}
}
TY - JOUR T1 - The Application of Partially Functional Linear Regression Model in Health Science AU - Weiwei Xiao AU - Yixuan Wang Y1 - 2020/11/04 PY - 2020 N1 - https://doi.org/10.11648/j.sd.20200806.13 DO - 10.11648/j.sd.20200806.13 T2 - Science Discovery JF - Science Discovery JO - Science Discovery SP - 134 EP - 138 PB - Science Publishing Group SN - 2331-0650 UR - https://doi.org/10.11648/j.sd.20200806.13 AB - 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. VL - 8 IS - 6 ER -
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