Scientific Data Services

"Advances in gradient boosting approaches for geoadditive models" is the title of Lars Knieper talk, which is being organised by the "Zentrum für Statistik" at Bielefeld University. It will take place on Tuesday, 27 May 2025 from 12:00 to 13:00 in W9-109.  

One of the key features of model-based component-wise gradient boosting is a data-driven variable selection mechanism which takes place while estimating the effects simultaneously. When spatial effects, of areal or point-reference data, are included as a potential model-component a drastic increase of chosen fixed effects can be observed. This is accompanied by a high selection frequency of the spatial component without achieving an impactful reduction of the loss function. To address this ineffective variable selection, we propose to eliminate the competition between fixed and spatial effects by separating the spatial part from the component-wise mechanism and ensuring complete estimation in each iteration. Additionally, we suggest using spatial cross-validation, which accounts for the auto-correlated structure of spatial data when constructing folds. Our approach is applied to yield deviation estimates of coffee farmers in Colombia, resulting in a sparser model with improved predictive performance. In addition, there will be an extended outlook on spatial confounding—an estimation bias arising from collinearity between fixed and spatial effects—which tends to be more pronounced in gradient boosting than in maximum likelihood approaches. A straightforward remedy, known as Spatial+, proves effective in mitigating this issue.