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 $[0,\infty ).$ 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.

摘要

概率密度函数的连续关联核估计中的贝叶斯带宽选择是交叉验证技术等经典方法的非常好的替代方法。在本文中，我们研究了 Wansouwé等人从理论上发展的伽玛核估计中贝叶斯变量带宽的行为。[ Ake：用于离散和连续关联核估计的 R 包，R 期刊 8 (2016)，第 259–276 页]，并适用于平滑支持密度$[0,\infty ).$仿真研究指出，与全局交叉验证带宽选择相比，在综合平方误差下，所提出的方法具有显着的性能。最终分别提出了涉及CO ₂排放和人身伤害理赔医疗费的两项申请。