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Survival Analysis using Bayesian Additive RegressionTrees
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Fatemeh Hosseini *   , Omid Karimi , Fatemeh Hamedi |
| Faculty of Mathematics, Statistics and Computer, Department of Statistics |
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Abstract: (3105 Views) |
| Tree models represent a new and innovative way of analyzing large data sets by dividing predictor space into simpler areas. Bayesian Additive Regression Trees model, a model that we explain in this article, uses a totality of trees in its structure, since the combination of several trees from a tree only has a higher accuracy.
Then, this model is a tree-based model and a nonparametric model that uses general aggregation methods, and boosting algorithms in particular and in fact is extension of the classification and Regression Tree methods in which the decision tree exists in the structure of these methods.
In this method, on the parameters of the model sum of tree and put regular prior then use the boosting algorithms for analysis. In this paper, first the Bayesian Additive Regression Trees model is introduced and then applied in survival analysis of lung cancer patients. |
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| Keywords: Tree Models, Bayesian Additive Model, Decision Tree. |
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Full-Text [PDF 1328 kb]
(1211 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2019/01/8 | Accepted: 2019/09/21 | Published: 2019/10/22
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