The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Model-Based Recursive Partitioning
Abstract
Recursive partitioning is embedded into the general and well-established class of parametric models that can be fitted using M-type estimators (including maximum likelihood). An algorithm for model-based recursive partitioning is suggested for which the basic steps are: (1) fit a parametric model to a dataset; (2) test for parameter instability over a set of partitioning variables; (3) if there is some overall parameter instability, split the model with respect to the variable associated with the highest instability; (4) repeat the procedure in each of the daughter nodes. The algorithm yields a partitioned (or segmented) parametric model that can be effectively visualized and that subject-matter scientists are used to analyzing and interpreting.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Computation and analysis of multiple structural change models
Bai, J., & Perron, P. (2003) · Journal of Applied Econometrics · doi:10.1002/jae.659 · Cited by 5,771
Tests for parameter instability and structural change with unknown change point.
Andrews, D. W. K. (1993) · Econometrica · doi:10.2307/2951764 · Cited by 4,334
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
Self-citation
Cited by
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917
Strobl, C., Malley, J., & Tutz, G. (2009) · Psychological Methods · doi:10.1037/a0016973 · Cited by 2,583
Cribari-Neto, F., & Zeileis, A. (2010) · Journal of Statistical Software · doi:10.18637/jss.v034.i02 · Cited by 2,505
Self-citationRecursive partitioning for heterogeneous causal effects
Athey, S., & Imbens, G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510489113 · Cited by 1,586
Machine Learning Methods That Economists Should Know About
Athey, S., & Imbens, G. W. (2019) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053433 · Cited by 1,163
Metadata
- Authors
- Zeileis, A., Hothorn, T., & Hornik, K.
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 2008-06
- Volume
- 17
- Issue
- 2
- Pages
- 492-514
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W2045803758
- Cited by
- 781
- References
- 5
- FWCI
- 9.62
- Type
- journal article
- Language
- en
- Open access
- closed
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