Yuri Fonseca, Marcelo Medeiros, Gabriel Vasconcelos, Alvaro Veiga
arXiv 10 Aug 2018 · Statistics — Machine Learning · 5 citations (OpenAlex)
arXiv:1808.03698 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Friedman (2001) `Greedy function approximation: a gradient boosting machine', Annals of statistics pp. 1189–1232 | 0.874 | 5 | 2 | 100% |
| 2 | Zhang, Yu et al (2005) `Boosting with early stopping: Convergence and consistency', The Annals of Statistics 33(4), 1538–1579 | 0.644 | 4 | 1 | 100% |
| 3 | Breiman (2001) `Random forests', Machine learning 45(1), 5–32 | 0.644 | 2 | 2 | 100% |
| 4 | Bühlmann (2002) Consistency for l2boosting and matching pursuit with trees and tree-type basis functions, in `Research report/Seminar für Statis… | 0.644 | 2 | 2 | 100% |
| 5 | Da Rosa, Veiga \ Medeiros (2008) `Tree-structured smooth transition regression models', Computational Statistics & Data Analysis 52(5), 2469–2488 | 0.644 | 2 | 2 | 100% |
| wager2014asymptotic | unmatched citation key wager2014asymptotic | 0.511 | 2 | 1 | 100% |
| 7 | Altonji, Ichimura \ Otsu (2012) `Estimating derivatives in nonseparable models with limited dependent variables', Econometrica 80(4), 1701–1719 | 0.405 | 1 | 1 | 100% |
| 8 | Bartlett \ Traskin (2007) `Adaboost is consistent', Journal of Machine Learning Research 8(Oct), 2347–2368 | 0.405 | 1 | 1 | 100% |
| bissantz2007convergence | unmatched citation key bissantz2007convergence | 0.405 | 1 | 1 | 100% |
| 10 | Breiman (1996) `Bagging predictors', Machine learning 24(2), 123–140 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 27 scored citations. 2 of these could not be matched to a bibliography entry, so only the citation key is shown.
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Forward-Selected Panel Data Approach for Program Evaluation | 0.405 | 1 | 1 |
| 2 | Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings? | 0.405 | 1 | 1 |
| 3 | Automatic Locally Robust GMM with Machine-Learning-Generated Regressors | 0.000 | 1 | 1 |