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### 有序响应变量的贝叶斯模型选择及其在COPD疾病防治中的应用

• 出版日期:2020-03-25 发布日期:2020-03-24

### Bayesian Model Selection of Ordinal Response Variables and Its Application in Disease Prevention of COPD

Zhao Weihua Wang Ling Hu Danqing Feng Junfeng

• Online:2020-03-25 Published:2020-03-24

Abstract: Chronic Obstructive Pulmonary Disease (COPD) is a disease with high morbidity and mortality. The diagnosis and severity grading of COPD depend on the examination of lung functions. However, due to the high cost of lung function examination instruments, this examination is not widely used in many economically underdeveloped areas, especially in rural grassroots hospitals. This paper studies how grassroots and communities can preliminarily identify COPD conditions with ordinal response model, so as to improve the level of prevention and treatment of COPD in grassroots and communities in China. Based on the Bayesian variable selection method and the data augmentation approach of latent variable, we obtain the Gibbs posterior sampling algorithm. Numerical simulation analysis further illustrates the usefulness of the proposed Bayesian model selection method for ordinal response variables, and the sparse models are given to determine the severity of COPD for real data analysis.