2018-11-13 14:00 — 15:00
Middle Lecture Room
School of Mathematics and Statistics Yunnan University
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.