TY  - INPR
A1  - Franke, Jürgen
A1  - Stockis, Jean-Pierre
A1  - Tadjuidje, Joseph
A1  - Li, W.K.
T1  - Mixtures of Nonparametric Autoregression, revised
N2  - 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 characterizing 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.
T3  - Report in Wirtschaftsmathematik (WIMA Report) - 121 
KW  - nonparametric regression
KW  - mixture
KW  - hidden variables
KW  - EM algorithm
Y1  - 2009
UR  - https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2115
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-16107
ER  -