嘉宾简介：

陈家骅教授，加拿大英属哥伦比亚大学国家一级科研讲座教授，云南大学数学与统计学院“计划”特聘教授，国际著名统计学家。2005-2006年任泛华统计学会主席。2010-2012任加拿大统计杂志主编。在混合模型，试验设计，经验似然，大样本理论和变量选择等多个统计研究领域有杰出的科研成就，在Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of the Royal Statistical Society (series B) 等国际顶尖统计杂志上发表了学术论文100多篇。

讲座简介:

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 tests such as Wilcoxon and Kolmogorov-Smirnov tests are found to have severely inflated type I errors when the data are clustered. There is an urgent need for a new test that performs well in the presence of random effects. In this talk, I present a novel test which combines composite empirical likelihood, cluster-based bootstrapping and density ratio model. This test satisfactorily controls the type I error in monitoring the trend of lower quantiles and conclusions are supported by asymptotic results. Although it was motivated by the aforementioned problem, this new test has other applications beyond the problem of the wood industry.