In the last decades the interest in statistical methods based on information measures and particularly in pseudodistances or divergences has grown substantially. Minimization of a suitable pseudodistance or divergence measure gives estimators (minimum pseudodistance estimators or minimum divergence estimators) that have nice robustness properties in relation to the classical maximum likelihood estimators with a not significant lost of efficiency. For more details we refer the monographs of Basu et al. [1] and Pardo [2]. Parametric test statistics based on the minimum divergence estimators have also given interesting results in relation to the robustness in comparison with the classical likelihood ratio test, Wald test statistic and Rao’s score statistic. Worthy of special mention are the Wald-type test statistics obtained as an extension of the classical Wald test statistic. These test statistics are based on minimum divergence estimators instead of the maximum likelihood estimators and have been considered in many different statistical problems: Censoring, see Ghosh et al. [3], equality of means in normal and lognormal models, see Basu et al. [4,5], logistic regression models, see Basu et al. [6], polytomous logistic regression models, see Castilla et al. [7], composite likelihood methods, see Martín et al. [8], etc.
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