Date: 2016-10-21

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Multi-level hierarchical clustered data are commonly seen in financial and biostatistics applications. In this talk, we introduce several modeling strategies for describing the dependent relationships for members within a cluster or between different clusters (in the same or different levels). In particular we will apply the hierarchical Kendall copula, first proposed by Brechmann (2014), to model two-level hierarchical clustered survival data. This approach provides a clever way of dimension reduction in modeling complicated multivariate data. Based on the model assumptions, we propose statistical inference methods, including parameter estimation and a goodness-of-fit test, suitable for handling censored data. Simulation and data analysis results are also presented.

Speaker

Chien-Lin Su is a postdoc fellow under the supervision of Professor Russell Steele (McGill) and Lajmi Lakhal-Chaieb (Laval). He received his Master’s degree in mathematics in 2009 and PhD degree in statistics from National Chiao Tung University (NCTU), Taiwan in 2015. His research interests include multivariate survival analysis and copula research in biomedical and financial applications. He received a grant from National Science Council (NSC) of Taiwan and conducted research as a research trainee with the supervision of Professor Johanna G. Nešlehová from July 2013 to February 2014.