Abstract
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 support Simulations 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.
Acknowledgements
The author sincerely thank an Associate Editor and two anonymous referees for their valuable comments. We are also grateful to Khoirin Nisa for her attentive reading.