Abstract:Markov Chains with variable length are useful stochastic models for data compression that avoid the curse of dimensionality faced by that full Markov Chains. In this paper we introduce a Variable Length Markov Chain whose transition probabilities depend not only on the state history but also on exogenous covariates through a logistic model. The goal of the proposed procedure is to obtain the context of the process, that is, the history of the process that is relevant for predicting the next state, together wit… Show more
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