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DTSTAMP:20260610T094418Z
DESCRIPTION:Enriched post-selection models for high dimensional data\n\n\n	A
 bstract:\n\n\nHigh dimensional data are rapidly growing in many domains\, 
 for example\, in microarray gene expression studies\, fMRI data analysis\,
  large-scale healthcare analytics\, text/image analysis\, natural language
  processing and astronomy\, to name but a few. In the last two decades reg
 ularisation approaches have become the methods of choice for analysing hig
 h dimensional data. However\, obtaining accurate estimates and predictions
  as well as valid statistical inference remains a major challenge in high 
 dimensional situations. In this talk\, we present enriched post-selection 
 models that aim to improve parameter estimation and prediction\, and to fa
 cilitate statistical inferences in high dimensional regression models. The
  enriched post-selection method enables us to construct valid post-selecti
 on inference for regression parameters in high dimensions. We discuss the 
 empirical and asymptotic properties of the enriched post-selection method.
 \n\n\n	Speaker\n\n\nDr. Reza Drikvandi is an Assistant Professor of Statist
 ics from the Department of Mathematical Sciences at Durham University.\n\n
 His research mainly focuses on high dimensional statistics\, longitudinal 
 data analysis\, mixed-effects models\, nonparametric and semiparametric mo
 dels\, joint modelling\, model diagnostics and missing data problems.\n\nM
 cGill Statistics Seminar schedule: https://mcgillstat.github.io/\n\nhttps:
 //mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09\n\nMee
 ting ID: 834 3668 6293\n\nPasscode: 12345\n\n \n
DTSTART:20220408T193000Z
DTEND:20220408T203000Z
SUMMARY:Reza Drikvandi (Durham University)
URL:https://www.mcgill.ca/mathstat/channels/event/reza-drikvandi-durham-uni
 versity-338906
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