SMS scnews item created by Munir Hiabu at Mon 15 Jun 2020 1136
Type: Seminar
Distribution: World
Expiry: 22 Jun 2020
Calendar1: 19 Jun 2020 1600-1700
CalLoc1: https://au.bbcollab.com/guest/fcf219c74ac743e89565a9e6e8d349a9
CalTitle1: Spatial Confounding in GEEs
Auth: munir@119-18-2-42.771202.syd.nbn.aussiebb.net (mhia8050) in SMS-WASM

# Statistics Across Campuses: Francis Hui -- Spatial Confounding in GEEs

Spatial Confounding in GEEs

Date: 19 June 2020, Friday

Time: 4pm

Speaker: Dr Francis Hui (ANU)

Abstract:

Generalized Estimating Equations (GEEs) are a popular tool in many scientific
disciplines for investigating the effects of covariates on the mean of a response.  In
the context of spatial analysis, GEEs rely on specifying a regression model for the
marginal mean, a variance function, and a working correlation matrix characterizing the
spatial correlation between observations.  One of the key features of GEEs is that
estimation of the covariate effects is robust to misspecification of the (spatial)
working correlation matrix.  That is, the choice of working correlation only affects
efficiency and not the consistency (effectively, the target) of the GEE estimator.

In this talk, we introduce and explore the concept of spatial confounding in GEEs.
Specifically, we show that in settings where the covariates included in the GEE are
(also) spatially correlated, the choice of working correlation can in fact change the
target coefficients one is estimating.  Effectively, this arises due to an implicit
multicollinearity occurring between the spatially correlated covariates and (the spatial
effect induced by) the working correlation matrix.  We propose the idea of a
â€œrestricted spatial working correlation matrixâ€, which estimates a so-called
unpartitioned effect that pulls all the variability in the direction of the covariates
into the marginal mean, and argue that is perhaps more in line with the underlying aim
of GEEs.  If time permits, we will touch upon the issues of standard error estimation as
well as large sample properties.

https://au.bbcollab.com/guest/fcf219c74ac743e89565a9e6e8d349a9


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