The new S language: a programming environment for data analysis and graphics
The new S language: a programming environment for data analysis and graphics
Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Machine Learning
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Hierarchical priors for Bayesian CART shrinkage
Statistics and Computing
Functional Models for Regression Tree Leaves
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Modern Applied Statistics with S
Modern Applied Statistics with S
A logistic regression framework for information technology outsourcing lifecycle management
Computers and Operations Research
Lake classification to enhance prediction of eutrophication endpoints in Finnish lakes
Environmental Modelling & Software
Discovering and understanding change using multivariate trees: the RECPAM approach
MATH'05 Proceedings of the 8th WSEAS International Conference on Applied Mathematics
Gaussian processes and limiting linear models
Computational Statistics & Data Analysis
Searching a multivariate partition space using MAX-SAT
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Temporal multi-hierarchy smoothing for estimating rates of rare events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Dirichlet Process Mixtures of Generalized Linear Models
The Journal of Machine Learning Research
Integrating classification trees with local logistic regression in Intensive Care prognosis
Artificial Intelligence in Medicine
Multi-output local Gaussian process regression: Applications to uncertainty quantification
Journal of Computational Physics
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When simple parametric models such as linear regression fail to adequately approximate a relationship across an entire set of data, an alternative may be to consider a partition of the data, and then use a separate simple model within each subset of the partition. Such an alternative is provided by a treed model which uses a binary tree to identify such a partition. However, treed models go further than conventional trees (e.g. CART, C4.5) by fitting models rather than a simple mean or proportion within each subset. In this paper, we propose a Bayesian approach for finding and fitting parametric treed models, in particular focusing on Bayesian treed regression. The potential of this approach is illustrated by a cross-validation comparison of predictive performance with neural nets, MARS, and conventional trees on simulated and real data sets.