Selected Estimated Models with ϕ-Divergence Statistics

When testing for discriminating between two competing models, a statistical method, usually, proceeds by evaluating the measure for discrepancy between the observed data and each parametric model. The parameter model with smaller value of measure statistic is generally chosen. This paper addresses the question of testing for choosing between two estimated models using some ϕ-divergence type statistics. We choice for arbitrary √n-asymptotically normal estimators to be used for introducing these statistics. The results here are illustrated by a simulation study, then Large Sample theory and bootstrap methods are used to construct our ϕ-divergence tests in parametric models.