TITLE: Semiparametric Monitoring test based on clustered data
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 talk, 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.
BIO: Prof. Jiahua Chen obtained his Ph.D degree in 1990 under the supervision of Professor C.F. Jeff Wu. He joined the Department of Statistics and Actuarial Science at the University of Waterloo in Ontario, Canada until 2006. In Jan 2007, Professor Chen was appointed as Canada Research Chair, Tier I at the University of British Columbia at Vancouver. Dr. Chen has broad research interest in statistics. He earned his Ph.D degree with a thesis on the design of experiment, started work on sampling survey problem soon after, got interested in empirical likelihood under the influence of his friend, and was guided into the area of mixture models by Professor Kalbfleisch. He is also working statistical genetic problems and gets involved in a clinical trial.