
Central Limit Theorem and Moderate deviation for nonhomogenenous Markov chains
Our purpose is to prove central limit theorem for countable nonhomogeneo...
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Selective Monitoring
We study selective monitors for labelled Markov chains. Monitors observe...
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Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models
This paper proposes a stochastic model using the concept of Markov chain...
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Data Reduction in Markov model using EM algorithm
This paper describes a data reduction technique in case of a markov chai...
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Estimations of means and variances in a Markov linear model
Multivariate regression models and ANOVA are probably the most frequentl...
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Improving graduation rate estimates using regularly updated Markov chains
American universities use a rolling sixyear graduation rate (SYGR) to c...
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Unified Method for Markov Chain Transition Model Estimation Using Incomplete Survey Data
The Future Elderly Model and related microsimulations are modeled as Mar...
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Variable Length Markov Chain with Exogenous Covariates
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 with the estimated coefficients corresponding to the significant exogenous variables. We show that the proposed method is consistent in the sense that the probability that the estimated context and the coefficients are equal to the true data generating mechanism tend to 1 as the sample size increases. Simulations suggest that, when covariates do contribute for the transition probability, the proposed procedure outperforms variable length Markov Chains that do not consider covariates while yielding comparable results when covariates are not present.
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