Webinar: Sophie Dabo

Oct 25, 2021, 11:00 am1:00 pm
Event Description

PASRC's multidisciplinary webinar is a monthly webinar organized by the PASRC's Research Committee. It aims to promote African research teams and foster multidisciplinary collaboration between them. During these sessions, the speaker provides an overview of his/her work, or his/her team's research, keeping in mind the multidisciplinary audience and perspective.

2. Title: Spatial Econometric modeling and Applications to environment and health
Date: October 25 at 11 a.m. EST.
Speaker: Sophie Dabo, University of Lille

Complex issues arise in spatial statistics and econometrics (statistical techniques to address economic modeling), many of which are neither clearly defined nor completely resolved but form the basis for current research. Among the practical considerations that influence the available methods used in spatial data modeling, particularly in econometrics, is data dependency. In fact, spatial data are often dependent, and a spatial model must be able to account for this characteristic. Linear spatial models, which are common in geostatistical modeling, generally impose a dependency structure model based on linear covariance relationships between spatial locations. However, under many circumstances, the spatial index does not vary continuously and may be of the lattice type, the baseline of this current talk.

This is, for instance, the case in a number of problems. In images analysis, environment, agriculture, health and so one, data are often received as regular lattice and identified as the centroids of square pixels, whereas a mapping forms often an irregular lattice. Basically, statistical models for lattice data are linked to nearest neighbors to express the fact that data are nearby.  We are concerned here about spatial real-valued or functional principal component analysis (PCA) and regression models for lattice data. We consider spatial PCA and a spatial linear regression models using spatial autoregression on the response or covariate based on a weight matrix.

We investigate finite and infinite sample properties of the regression parameters estimators using the so-called increasing domain asymptotic. We provide applications to environmental and health problems.

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