References
- Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and regression trees, Wadsworth, Belmont, CA.
- Cappelli, C. and Iorio, D. (2010). Detecting contemporaneous mean co-breaking via ART and PCA. Quaderni di STATISTICA, 12, 169-184.
- Chang, Y. and Kim, H. (2011). Tree-structured nonlinear regression. The Korean Journal of Applied Statistics, 24, 759-768. https://doi.org/10.5351/KJAS.2011.24.5.759
- Charbonneau, K. B. (2014). Multiple fixed effects in binary response panel data models, The Bank of Canada Working paper, The Bank of Canada, Canada.
- Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of American Statistical Association, 74, 829-836. https://doi.org/10.1080/01621459.1979.10481038
- De'ath, G. (2002). Multivariate retression trees: A new technique for modeling species-environment relationships. Ecology, 83, 1105-1117.
- De'ath, G. (2013). mvpart: Multivariate partitioning, R package version 1.6-1. Available from http://CRAN.R-project.org/package=mvpart.
- Dzeroski, S. and Zenko, B. (2004). Is combining classifiers with stacking better than selecting the best one? Machine Learning, 54, 255-273. https://doi.org/10.1023/B:MACH.0000015881.36452.6e
- Jo, J. and Chang, U. J. (2013). A statistical analysis of the fat mass repeated measures data using mixed model. Journal of the Korean Data & Information Science Society, 24, 303-310. https://doi.org/10.7465/jkdi.2013.24.2.303
- Lee, S. K. (2005). On generalized multivariate decision tree by using GEE. Computational Statistics and Data Analysis, 49, 1105-1119. https://doi.org/10.1016/j.csda.2004.07.003
- Loh, W.-Y. (2002). Regression trees with unbiased variable selection and interaction detection. Statistics Sinica, 12, 361-386.
- Loh, W.-Y. and Zheng, W. (2013). Regression trees for longitudinal and multiresponse data. The Annals of Applied Statistics, 7, 495-522. https://doi.org/10.1214/12-AOAS596
- Meek, C., Chickering, D. M. and Heckerman, D. (2002). Autoregressive tree models for time-series analysis. Proceedings of the Second International SIAM Conference on Data Mining, 229-244.
- Rea, W. S., Relae M., Cappelli, C. and Brown J. A. (2010). Identification of changes in mean with regression trees: An application to market research. Econometric Reviews, 29, 754-777. https://doi.org/10.1080/07474938.2010.482001
- Segal, M. R. (1992). Tree structured methods for longitudinal data. Journal of American Statistical Association, 87, 407-418. https://doi.org/10.1080/01621459.1992.10475220
- Sela, R. J. and Simonoff, J. S. (2012). RE-EM trees: A data mining approach for longitudinal and clustered data. Machine Learning, 86, 169-207. https://doi.org/10.1007/s10994-011-5258-3
- Zhang, H. (1998). Classification trees for multiple binary responses. Journal of American Statistical Association, 93, 180-193. https://doi.org/10.1080/01621459.1998.10474100
Cited by
- An analysis of changes in the influence of GDP gap on inflation vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1377
- Study of child abuse families using logistic regression models vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1327
- How depression affects girls who experienced violence in home or at school: Using mixed model vol.27, pp.1, 2016, https://doi.org/10.7465/jkdi.2016.27.1.101
- A spatial panel regression model for household final consumption expenditure based on KTX effects vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1147