@article{ZIANE_Zougab_Adjabi_2022, title={The adaptive gamma-BSPE kernel density estimation for nonnegative heavy-tailed data: Adaptive gamma-BSPE kernel density estimation for nonnegative heavy-tailed data}, volume={2}, url={https://jiamcs.centre-univ-mila.dz/index.php/jiamcs/article/view/v2i2_32}, DOI={10.58205/jiamcs.v2i2.32}, abstractNote={<p>In this work, we consider the nonparametric estimation of the probability density function for nonnegative heavy-tailed (HT) data. The objective is first to propose a new estimator that will combine two regions of observations (high and low density). While associating a gamma kernel to the high-density region and a BS-PE kernel to the low-density region. Then, to compare the proposed estimator with the classical estimator in order to evaluate its performance. The choice of bandwidth is investigated by adopting the popular cross-validation technique and two variants of the Bayesian approach. Finally, the performances of the proposed and the classical estimators are illustrated by a simulation study and real data.</p>}, number={2}, journal={Journal of Innovative Applied Mathematics and Computational Sciences}, author={ZIANE, Yasmina and Zougab, Nabil and Adjabi, Smail}, year={2022}, month={Sep.}, pages={38–47} }