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DC Field | Value | Language |
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dc.contributor.author | Jing Dai | en_US |
dc.contributor.author | Cheng Zi | en_US |
dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.contributor.author | Zhanqiong He | en_US |
dc.date.accessioned | 2018-09-04T09:25:35Z | - |
dc.date.available | 2018-09-04T09:25:35Z | - |
dc.date.issued | 2013-01-01 | en_US |
dc.identifier.issn | 21945357 | en_US |
dc.identifier.other | 2-s2.0-84872796130 | en_US |
dc.identifier.other | 10.1007/978-3-642-35443-4-22 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84872796130&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/52461 | - |
dc.description.abstract | China's economy has experienced remarkable growth in past 20 years. With rapid economic growth, Chinese people have enjoyed significant nutritional improvements. Meanwhile, with the changes in lifestyle, dietary behavior and other aspects, the prevalence of obesity and high blood pressure has also increased quickly. The relationship between obesity, high blood pressure and risk of chronic noncommunicable diseases is continuous and consistent. The higher BMI and blood pressure, the greater the chance of heart attack, stroke, kidney disease and etc. The objective of this paper is to analyze how socio-demographic and socioeconomic factors affect the prevelance of obesity and high blood pressure, and find the dependence structure between obesity and high blood pressure (HBP) with the help of copula functions. Computational results were obtained by R programme, and the results show that Frank copula model provides a better estimation than others. The empirical findings of this paper provide useful insights which can be expected to be of interest to public health sectors and local government in the formulation of health management policies especially on obesity, high blood pressure, and related chronic non-communicable disease. © 2013 Springer-Verlag Berlin Heidelberg. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Engineering | en_US |
dc.title | Analyzing dependence structure of obesity and high blood pressure: A copula approach | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Advances in Intelligent Systems and Computing | en_US |
article.volume | 200 AISC | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
article.stream.affiliations | Kunming University of Science and Technology | en_US |
Appears in Collections: | CMUL: Journal Articles |
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