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DTSTAMP:20260601T040403Z
DESCRIPTION:Statistical Analysis of Two-Level Hierarchical Clustered Data\n
 \nMulti-level hierarchical clustered data are commonly seen in financial a
 nd biostatistics applications. In this talk\, we introduce several modelin
 g strategies for describing the dependence relationships for members withi
 n a cluster or between different clusters (in the same or different levels
 ). In particular\, we will apply the hierarchical Kendall copula\, first p
 roposed by Brechmann (2014)\, to model two-level hierarchical clustered su
 rvival 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. Simulati
 on and data analysis results are also presented.\n
DTSTART:20170316T173000Z
DTEND:20170316T183000Z
LOCATION:Room VCH-3840\, CA\, QC\, Sherbrooke\, Seminar Statistique Sherbro
 oke\, 2500 Boul de L'Université
SUMMARY:Chien-Lin Su\, 91ºÚÁÏÍø- Université Laval
URL:/mathstat/channels/event/chien-lin-su-mcgill-unive
 rsity-universite-laval-267011
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