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DTSTAMP:20260415T111949Z
DESCRIPTION:Title: Empirical likelihood and robust regression in diffusion 
 tensor imaging data analysis\n\nAbstract: With modern technology developme
 nt\, functional responses are observed frequently in various scientific fi
 elds including neuroimaging data analysis. Empirical likelihood as a nonpa
 rametric data-driven technique has become an important statistical inferen
 ce methodology. In this paper\, motivated by diffusion tensor imaging (DTI
 ) data we propose three generalized empirical likelihood-based methods tha
 t accommodate within-curve dependence on the varying coefficient model wit
 h functional responses and embed a robust regression idea. To avoid the lo
 ss of efficiency in statistical inference\, we take into consideration wit
 hin-curve variance-covariance matrix in the subjectwise and elementwise em
 pirical likelihood methods. We develop several statistical inference proce
 dures for maximum empirical likelihood estimators (MELEs) and empirical lo
 g likelihood (ELL) ratio functions\, and systematically study their asympt
 otic properties. We first establish the weak convergence of the MELEs and 
 the ELL ratio processes\, and derived a nonparametric version of the Wilks
  theorem for the limiting distributions of the ELLs at any designed point.
  We propose a global test for linear hypotheses of varying coefficient fun
 ctions and construct simultaneous confidence bands for each individual eff
 ect curve based on MELEs\, and construct simultaneous confidence regions f
 or varying coefficient functions based on ELL ratios. A Monte Carlo simula
 tion is conducted to examine the finite-sample performance of the proposed
  procedures. Finally\, we illustrate the estimation and inference procedur
 es on MELEs of varying coefficient model to a diffusion tensor imaging dat
 a from Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Joint wor
 k with Xingcai Zhou (Nanjing Audit University)\, Rohana Karunamuni and Ada
 m Kashlak (University of Alberta).\n\nThis is joint work with Ru Zhang at 
 Queen’s University and Pritam Ranjan at Indian Institute of Management Ind
 ore.\n
DTSTART:20180406T193000Z
DTEND:20180406T203000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Professor Linglong Kong (University of Alberta)
URL:https://www.mcgill.ca/mathstat/channels/event/professor-linglong-kong-u
 niversity-alberta-286413
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