报告时间：2016年11月19日（周六）上午10：00

报告地点：教二楼202

报  告  人：Jiahua Chen (陈家骅) ，云南大学大数据研究院&Department of Statistics and Actuarial Science, University of British Columbia, Canada.

报告题目：Large Semiparametric Monitoring Test Based on Clustered Data

报告摘要：Due to factors such as climate change, forest fire, plague of insects on lumber quality, it is important to update (statistical) procedures in American Society for Testing and Materials (ASTM) Standard D1990 (adopted in 1991) from time to time. The statistical component of the problem is to detect the change in the lower percentiles of the solid lumber strength. Verrill et al. (2015) studied eight statistical tests proposed by wood scientists to determine if they perform acceptably when applied to test data from a monitoring program. Some well-known methods such as Wilcoxon and Kolmogorov-Smirnov tests are found to have severely inflated type I errors when the data are clustered. A new method that performs well in the presence of random effects is therefore in urgent need. In this paper, we develop a novel test by combining composite empirical likelihood, cluster-based bootstrapping and density ratio model. The test satisfactorily controls the type I error in monitoring the trend of lower quantiles and conclusions are supported by asymptotic results. Our results are generic, not confined to wood industry applications.