Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

Cet article propose l'utilisation de la méthode de l'Entropie Maximale Généralisée (GME) pour estimer la répartition des inputs (et des coûts de production) entre différents produits en utilisant des sources de données hétérogènes (données comptables agricoles et données de...

We develop a Bayesian estimation framework for non-stationary Markov models for situations where both sample data on observed transitions between states (micro data) and population data, where only the proportion of individuals in each state is observed (macro data), are available. Posterior...

In this poster a Bayesian estimation framework for a non-stationary Markov model is developed for situations where sample data with observed transition between classes (micro data) and aggregate population shares (macro data) are available. Posterior distributions on transition probabilities are...

A recent article (Pavelescu, 2009) proposes a correction to the conventional student-t test of significance in linear regression models, but offers no formal description of its properties. This comment formally characterizes the sampling properties of the corrected student-t statistic. In...