Bayesian estimation procedure in multiprocess non-linear dynamic generalized model

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we extend the multiprocess dynamic generalized linear model to allow for a known non-linear parameter evolution and predictor functions. This is done by approximating the non-linear function by a linear function based on a first order Taylor series expansions. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.