Leandro Pardo

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In this paper we consider inference based on very general divergence measures, under assumptions of multinomial sampling and loglinear models. We define the minimum φ-divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. This estimator is then used in a φ-divergence goodness-of-fit statistic, which is the basis of(More)
For data generated by stationary Markov chains there are considered estimates of chain parameters minimizing ^-divergences between theoretical and empirical distributions of states. Consistency and asymptotic normality are established and the asymptotic covari-ance matrices are evaluated. Testing of hypotheses about the stationary distributions based on(More)
In this paper we study polytomous logistic regression model and the asymptotic properties of the minimum φ-divergence estimators for this model. A simulation study is conducted to analyze the behavior of these estimators as function of the power-divergence measure φ (λ) .