Estimation of the Mixture Transition Distribution Model
نویسنده
چکیده
This paper introduces a new iterative algorithm for the estimation of the Mixture Transition Distribution model (MTD). It does not require the use of any speciic external optimization procedure and can therefore be programmed in any computing language. Comparisons with previously published results show that this new algorithm performs at least as good or better than other methods. The choice of initial values is also discussed. The MTD model was designed for the modeling of high-order Markov c hains and already proved to be a useful tool for the analysis of diierent t ypes of time-series such as wind speeds and wind directions. In this paper, we also propose to use this it for the modeling of one-dimensional spatial data. An application using a DNA sequence shows that this approach can lead to better results than the classical Potts model.
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