Poisson分布下基于鞍点逼近的慢性病风险差的置信区间构造  

Confidence interval construction for the risk difference of chronic disease based on saddle-point approximation under poisson distribution

作　　者：白永昕 田茂再 

机构地区：[1]兰州财经大学统计学院,甘肃兰州730020 [2]中国人民大学应用统计科学研究中心中国人民大学统计学院,北京100972

出　　处：《高校应用数学学报（A辑）》2017年第3期253-266,共14页Applied Mathematics A Journal of Chinese Universities(Ser.A)

基　　金：教育部哲学社会科学研究重大课题攻关项目(15JZD015);教育部人文社会科学重点研究基地重大项目(15JJD910001);北京市社会科学基金重大项目(15ZDA17);国家社会科学基金重点项目(13AZD064);中央高校建设世界一流大学(学科)和特色发展引导专项资金支持(15XNL008);全国统计科研计划项目(2016LD03);兰州财经大学"兴隆学者特聘计划"

摘　　要：风险差是流行病学中重要的指标之一,常用来比较两种治疗或两种诊断的有效性.因此,风险差区间的精确估计对流行病病情的诊断以及治疗方案的选择有很重要的意义.结合Poisson抽样的优点以及慢性病发病周期长和发病率低的特点,利用鞍点逼近方法来构造了Poisson分布下风险差的置信区间.同时,通过实例和Monte Carlo模拟对传统的四种区间构造方法进行评价.模拟结果表明:在小样本情况下,鞍点逼近方法得到的置信区间大多数能保证覆盖率近似于期望的置信水平并且使得区间长度最短,是一种很好的置信区间构造方法.Risk is one of the important indicators in the epidemiology and usually used to compare the effectiveness of two therapeutics or diagnostics. Therefore, accurate estimate of the risk difference interval is important for the diagnosis of disease and selection of therapeutic scheme. Combining with the characteristics of chronic whose diseases cycle is long and the incidence is low and the advantages of Poisson sampling, the paper uses the saddle point approximation method to construct the risk difference confidence interval under the Poisson distribution. At the same time, five traditional kinds of interval estimation method are assessed through examples and monte carlo simulation. Simulation results show that under the condition of small samples, the saddle point approximation is a kind of very good confidence interval estimation method. In most cases it can guarantee the coverage rate being equal to the desired confidence level and make the shortest length.

关 键 词：POISSON分布 鞍点逼近 MONTE CARLO模拟 置信区间 

分 类 号：O212[理学—概率论与数理统计]