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Activity Number: 493 - New Statistical Methods for Lumber Analytics
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract #326592 Presentation
Title: Semiparametric Monitoring Test Based on Clustered Data
Author(s): Jiahua Chen* and Pengfei Li and yukun liu and Jim Zidek
Companies: University of British Columbia and University of Waterloo and East China Normal University and University of British Columbia
Keywords: Bootstrap; Composite likelihood; Density ratio model; Empirical likelihood; Multiple sample

Due to factors such as climate change, forest fire, the 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 present 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.

Authors who are presenting talks have a * after their name.

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