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The EViews software is used for statistical analysis of the data of registered cases of bronchitis and the average temperature in Haixizhou Region, Qinghai Province, at the same time, the independence tests and forecasts for the residue series are carried out to validate the established models. The optimized model, TARCH(1, 1)-M model, is obtained. The results show that this model can be best used to describe the relationship between the monthly incidence rate of bronchitis and the average temperature, to simulate the variation trend of the monthly incidence rate and to predict the future incidence trend, with a high prediction precision., authors=,, authorsList=;, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=/PDep3Mz1FOwMx0V6Kk12A==, pdfFileSize=663975, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242119351621452387, articleId=1242119348668666622, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于非对称ARCH模型的海西州地区支气管炎发病研究, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=基于非对称ARCH模型的理论，通过建立TARCH、TARCH-M、EGARCH和EGARCH-M模型研究青海省海西州地区支气管炎月发病率和平均气温之间的相互关系。研究通过EViews软件对海西州地区支气管炎发病病例和平均气温监测登记资料的统计分析，并利用原数据建立ARCH、TARCH-M、EGARCH和EGARCH-M模型，通过对模型中的残差序列进行独立性检验和预测，确定所建立的ARCH、TARCH-M、EGARCH和EGARCH-M模型的合理性，并从中选出了最优模型TARCH(1，1)-M。结果表明，此模型可以很好地描述海西州地区支气管炎月发病率与平均气温的关系，还可以较好地模拟支气管炎月发病率变化趋势，预测未来的支气管炎发病趋势，是一种预测精度较高的预测模型。该模型可为支气管炎研究提供有参考价值的数据。, authors=马亮亮,田富鹏, authorsList=马亮亮;田富鹏, authorCompany=西北民族大学计算机科学与信息工程学院，兰州 730030, correspAuthors=null, authorNote=null, correspAuthorsNote=马亮亮, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=4RLPmM/NRg8OwSYwPjZ/fw==, pdfFileSize=663975, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, 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