A hybrid method for density power divergence minimization with application to robust univariate location and scale estimation

A hybrid method for density power divergence minimization with application to robust univariate location and scale estimation
复制标题

密度功率散度最小化的混合方法,应用于稳健的单变量位置和尺度估计

DOI:
10.1080/03610926.2023.2209347
复制
发表时间:
2023
期刊:
Communications in Statistics - Theory and Methods
影响因子:
--
通讯作者:
Pokojovy, Michael
Pokojovy, Michael
中科院分区:
--
文献类型:
--
作者:
Anum, Andrews T.;Pokojovy, Michael

文献摘要

参考文献

相似文献

我们开发了一种新的全局收敛的优化方法来解决约束最小化问题的基础上的最小密度功率发散估计的单变量高斯数据中存在的离群值。我们的混合程序结合了经典的牛顿法与梯度下降迭代配备了一个步骤控制机制的基础上Armijo的规则,以确保全局收敛。广泛的模拟比较所得到的估计过程与更突出的强大的竞争对手,最小协方差行列式(MCD)估计,在广泛的击穿点值表明我们的方法提高了效率。应用程序的估计和推理的真实世界的数据集。
We develop a new globally convergent optimization method for solving a constrained minimization problem underlying the minimum density power divergence estimator for univariate Gaussian data in the presence of outliers. Our hybrid procedure combines classical Newton’s method with a gradient descent iteration equipped with a step control mechanism based on Armijo’s rule to ensure global convergence. Extensive simulations comparing the resulting estimation procedure with the more prominent robust competitor, Minimum Covariance Determinant (MCD) estimator, across a wide range of breakdown point values suggest improved efficiency of our method. Application to estimation and inference for a real-world dataset is also given.
DOI: 10.1080/01621459.1994.10476867
发表时间: 1994-12-01
影响因子: 3.7
作者:
CROUX, C;ROUSSEEUW, PJ;HOSSJER, O
通讯作者: HOSSJER, O
DOI: 10.1016/b978-0-12-386908-1.00037-9
发表时间: 2018-11
期刊: Wiley Series in Probability and Statistics
影响因子: --
作者:
Bruce E. Blaine
通讯作者: Bruce E. Blaine
DOI: --
发表时间: 2022
期刊: International Conference Smart Data and Smart Cities
影响因子: --
作者:
Michael Pokojovy;A. Anum
通讯作者: A. Anum
DOI: 10.1214/aos/1032526973
发表时间: 1996-06
影响因子: 4.5
作者:
J. Kent;David E. Tyler
通讯作者: J. Kent;David E. Tyler
应用 Heath-Jarrow-Morton 模型预测美国国债每日收益率曲线利率
DOI: --
发表时间: 2021
期刊: Mathematics
影响因子: 2.4
作者:
V. Maltsev;Michael Pokojovy
通讯作者: Michael Pokojovy