BART: Bayesian additive regression trees

Albert, J. H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. <i>J. Amer. Statist. Assoc.</i> <b>88</b> 669–679.

Freund, Y. and Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. <i>J. Comput. System Sci.</i> <b>55</b> 119–139.

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. <i>Ann. Statist.</i> <b>29</b> 1189–1232.

Denison, D. G. T., Mallick, B. K. and Smith, A. F. M. (1998). A Bayesian CART algorithm. <i>Biometrika</i> <b>85</b> 363–377.

Breiman, L. (1996). Bagging predictors. <i>Machine Learning</i> <b>26</b> 123–140.

Breiman, L. (2001). Random forests. <i>Machine Learning</i> <b>45</b> 5–32.

Amit, Y. and Geman, D. (1997). Shape quantization and recognition with randomized trees. <i>Neural Computation</i> <b>9</b> 1545–1588.

Blanchard, G. (2004). Un algorithme accelere d’echantillonnage Bayesien pour le modele CART. <i>Revue d’Intelligence artificielle</i> <b>18</b> 383–410.

Chang, C.-C. and Lin, C.-J. (2001). LIBSVM: A library for support vector machines. Available at <a href="http://www.csie.ntu.edu.tw/ cjlin/libsvm">http://www.csie.ntu.edu.tw/ cjlin/libsvm</a>.

Chipman, H. A., George, E. I. and McCulloch, R. E. (1998). Bayesian CART model search (with discussion and a rejoinder by the authors). <i>J. Amer. Statist. Assoc.</i> <b>93</b> 935–960.

Chipman, H. A., George, E. I. and McCulloch, R. E. (2002). Bayesian treed models. <i>Machine Learning</i> <b>48</b> 299–320.

Chipman, H. A., George, E. I. and McCulloch, R. E. (2007). Bayesian ensemble learning. In <i>Neural Information Processing Systems</i> <b>19</b> 265–272.

Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least angle regression (with discussion and a rejoinder by the authors). <i>Ann. Statist.</i> <b>32</b> 407–499.

Feng, J., Lurati, L., Ouyang, H., Robinson, T., Wang, Y., Yuan, S. and Young, S. (2003). Predictive toxicology: Benchmarking molecular descriptors and statistical methods. <i>Journal of Chemical Information and Computer Sciences</i> <b>43</b> 1463–1470.

Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion and a rejoinder by the author). <i>Ann. Statist.</i> <b>19</b> 1–67.

Green, P. J. (1995). Reversible jump MCMC computation and Bayesian model determination. <i>Biometrika</i> <b>82</b> 711–732.

Hastie, T. and Tibshirani, R. (2000). Bayesian backfitting (with comments and a rejoinder by the authors). <i>Statist. Sci.</i> <b>15</b> 196–223.

Kim, H., Loh, W.-Y., Shih, Y.-S. and Chaudhuri, P. (2007). Visualizable and interpretable regression models with good prediction power. <i>IEEE Transactions: Special Issue on Data Mining and Web Mining</i> <b>39</b> 565–579.

Wu, Y., Tjelmeland, H. and West, M. (2007). Bayesian CART: Prior specification and posterior simulation. <i>J. Comput. Graph. Statist.</i> <b>16</b> 44–66.

Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and testing for aggregation bias. <i>J. Amer. Statist. Assoc.</i> <b>57</b> 348–368.

Zhang, J. L. and Haerdle, W. K. (2010). The Bayesian additive classification tree applied to credit risk modelling. <i>Comput. Statist. Data Anal.</i> <b>54</b> 1197–1205.

Zhang, S., Shih, Y.-C. T. and Muller, P. (2007). A spatially-adjusted Bayesian additive regression tree model to merge two datasets. <i>Bayesian Anal.</i> <b>2</b> 611–634.

Zhou, Q. and Liu, J. S. (2008). Extracting sequence features to predict protein-DNA binding: A comparative study. <i>Nucleic Acids Research</i> <b>36</b> 4137–4148.

Venables, W. N. and Ripley, B. D. (2002). <i>Modern Applied Statistics with S</i>, 4th ed. Springer, New York.

Abreveya, J. and McCulloch, R. (2006). Reversal of fortune: A statistical analysis of penalty calls in the national hockey league. Technical report, Purdue Univ.

Abu-Nimeh, S., Nappa, D., Wang, X. and Nair, S. (2008). Detecting phishing emails via Bayesian additive regression trees. Technical report, Southern Methodist Univ., Dallas, TX.

Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D. and Weingessel, A. (2008). e1071: Misc functions of the Department of Statistics (e1071), TU Wien. R package version 1.5-18.

Ridgeway, G. (2004). The gbm package. R Foundation for Statistical Computing, Vienna, Austria.

Sing, T., Sander, O., Beerenwinkel, N. and Lengauer, T. (2007). ROCR: Visualizing the performance of scoring classifiers. R package version 1.0-2.