In particular, we specify a semiparametric Bayesian model for the observed data via Gaussian process priors and Bayesian additive regression trees. These model specifications allow us to capture nonlinear and nonadditive effects, in contrast to existing parametric methods. We then separately specify the ...
The approach is to specify models for the observed data using Bayesian additive regression trees, and then, use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health and ageing...
A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion. When used with decision trees, it uses regression trees to minimize the error of the prediction. A first tree predicts the probability of a data point to belong to a class; the next...
The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conducted by The Organization for Economic Co-operation and Development. This study compares five feature selection techniques to address this