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Bayesian bandwidth selections in continuous associated kernel estimation of probability density function are very good alternatives to classical methods like cross-validation techniques. In this paper, we examined the behavior of Bayesian variable bandwidths in gamma kernel estimation, developed theoretically in Wansouwé  et al.  [ Ake: An R Package for Discrete and Continuous Associated Kernel Estimations , The R journal 8 (2016), pp. 259–276], and appropriated to smooth densities of supportSimulations studies point out remarkable performance of the proposed approach, comparing to the global cross-validation bandwidth selection, and under integrated squared errors. Two applications related to  CO2  emissions and medical bills of bodily injury claims, respectively, are finally made.

Bayesian selector of adaptive bandwidth for gamma kernel density estimator on simulations and applications