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SSA Canberra: An ongoing journey into spatial confounding

  • 30 Mar 2021
  • 6:30 PM - 7:30 PM (AEDT)
  • Virtual via Zoom

SSA Canberra invites you to its March branch meeting, where the confused, outgoing president Francis Hui will take into his journey through the topic of spatial confounding.     

Times (+/- some standard errors): 

6:30pm-7:15pm: Outgoing El-presidente presentation on Zoom

Unfortunately, no virtual pre-drinks and nibbles will be provided this time around, as attendees (including the speaker!) will be coming directly from the SSA Canberra Annual General Meeting (AGM). All SSA Canberra members should have/will receive a separate invitation email to attend the Canberra branch AGM prior to the branch meeting.

RSVP: Please register in advance for this meeting at After registering, you will receive a confirmation email containing information about how to join the meeting. If you have any questions, please feel free to contact

Speaker: Francis Hui, Australian National University

Topic: My (Ongoing) Journey into Spatial Confusing Confounding

Abstract: In this general-ish talk, I will give semi-gentle introduction on an increasingly popular and at times controversial topic in spatio-temporal staistics, namely spatial confounding. Through a viewing and discussion of three select papers on the topic, I will motivate and explain what spatial confounding is and how it occurs, review a well-known solution to the problem known as restricted spatial regression (RSR), and subsequently discuss why RSR, and the issue of spatial confounding as a whole, has garnered some debate.            

Biography: Francis Hui is a Senior Lecturer in Statistics at the Australian National University. He completed his PhD at the University of New South Wales in 2014, moved to Canberra shortly afterwards to undertake a postdoctoral fellowship at the ANU, and has been happily stuck there ever since. His research spans a mixture of methodological, computational, and applied statistics, including longitudinal and correlated data analysis, dimension reduction and variable selection, and approximate statistical estimation and inference. Much of his research is motivated by joint modeling in ecology, and longitudinal analysis of mental health data, and is complemented by copious amounts of tea drinking, and unhealthy amounts of anime watching.

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