基于数据变换的两种新的非参数核分布估计器。
Two new nonparametric kernel distribution estimators based on a transformation of the data.
作者信息
Slaoui Yousri
机构信息
Laboratoire de Mathématiques et Application, Université de Poitiers, Futuroscope Chasseneuil, France.
出版信息
J Appl Stat. 2020 Jun 26;48(12):2065-2091. doi: 10.1080/02664763.2020.1786675. eCollection 2021.
In this paper, we propose two kernel distribution estimators based on a data transformation. We study the properties of these estimators and we compare them with two conventional estimators. It appears that with an appropriate choice of the parameters of the two proposed estimators, the convergence rate of two estimators will be faster than that of the two conventional estimators and the Mean Integrated Square Error will be smaller than the two conventional estimators. We corroborate these theoretical results through simulations as well as a real data set.
在本文中，我们基于数据变换提出了两种核分布估计器。我们研究了这些估计器的性质，并将它们与两种传统估计器进行了比较。结果表明，通过适当选择所提出的两种估计器的参数，这两种估计器的收敛速度将比两种传统估计器更快，且平均积分平方误差将小于两种传统估计器。我们通过模拟以及一个真实数据集证实了这些理论结果。
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