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Found 4 papers, 2 papers with code
Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate Datasets
In this article, we describe a transfer learning approach for building high-dimensional generalized linear models using data from a main study that has detailed information on all predictors, and from one or more external studies that have ascertained a more limited set of predictors.
Random forests for binary geospatial data
Binary geospatial data is commonly analyzed with generalized linear mixed models, specified with a linear fixed covariate effect and a Gaussian Process (GP)-distributed spatial random effect, relating to the response via a link function.
Inferring independent sets of Gaussian variables after thresholding correlations
Our proposed test properly accounts for the fact that the set of variables is selected from the data, and thus is not overly conservative.
Random Forests for dependent data
The key to this extension is the equivalent representation of the local decision-making in a regression tree as a global OLS optimization which is then replaced with a GLS loss to create a GLS-style regression tree.
