Minimum -divergence estimator and hierarchical testing in loglinear models under product-multinomial sampling

Abstract: Using Implicit Function Theorem, we get the asymptotic expansion and normality of the minimum -divergence estimator which is seen to be a generalization of the maximum likelihood estimator for loglinear models under product-multinomial sampling. Then we use and -divergence measures to construct statistics in order to solve some classical problems including testing nested hypotheses. In last section we apply this method to a real data and do some simulation study to show the validness of and assess the finite-sample performance among different . [Copyright &y& Elsevier]