Abstract
We consider data generating mechanisms which can be represented as mixtures of finitely many regression or autoregression models. We propose nonparametric estimators for the functions characterising the various mixture components based on a local quasi maximum likelihood approach and prove their consistency. We present an EM algorithm for calculating the estimates numerically which is mainly based on iteratively applying common local smoothers and discuss its convergence properties. Copyright © 2011 American Statistical Association and Taylor & Francis.
Original language | English |
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Pages (from-to) | 287-303 |
Journal | Journal of Nonparametric Statistics |
Volume | 23 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2011 |
Citation
Franke, J., Stockis, J.-P., Tadjuidje-Kamgaing, J., & Li, W. K. (2011). Mixtures of nonparametric autoregressions. Journal of Nonparametric Statistics, 23(2), 287-303. doi: 10.1080/10485252.2010.539686Keywords
- Nonparametric regression
- Nonparametric autoregression
- Mixture
- Hidden variables
- EM algorithm
- Kernel estimates
- Local likelihood