Mixtures of nonparametric autoregressions

J. FRANKE , J.-P. STOCKIS, J. TADJUIDJE-KAMGAING, Wai Keung LI

Research output: Contribution to journalArticle

9 Citations (Scopus)

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 languageEnglish
Pages (from-to)287-303
JournalJournal of Nonparametric Statistics
Volume23
Issue number2
DOIs
Publication statusPublished - 2011

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Autoregression
Local Likelihood
Quasi-maximum Likelihood
Nonparametric Estimator
EM Algorithm
Convergence Properties
Regression
Estimate
Model
EM algorithm
Estimator
Quasi-maximum likelihood

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: 10.1080/10485252.2010.539686

Keywords

  • Nonparametric regression
  • Nonparametric autoregression
  • Mixture
  • Hidden variables
  • EM algorithm
  • Kernel estimates
  • Local likelihood