BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions

Autor:	Fonseca, Yuri, Medeiros, Marcelo, Vasconcelos, Gabriel, Veiga, Alvaro
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Economics - Econometrics Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main advantage of the BooST model is the estimation of the derivatives (partial effects) of very general nonlinear models. Therefore, the model can provide more interpretation about the mapping between the covariates and the dependent variable than other tree-based models, such as Random Forests. We present several examples with both simulated and real data.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1808.03698 Zobrazit plný text záznamu View this record from Arxiv