Weighted kernel estimators in nonparametric binomial regression
Weighted kernel estimators in nonparametric binomial regression
复制标题
非参数二项式回归中的加权核估计量
DOI：
10.1080/10485250310001624828
复制
发表时间：
2004
影响因子：
1.2
通讯作者：
K. Naito
中科院分区：
文献类型：
--
作者：
Hidenori Okumura;K. Naito
本文研究非参数二项回归。提出了一种基于核的二项回归估计及其偏差调整版本，其中核由每个协变量的观测比例的方差估计的倒数加权。在一定的正则性条件下，证明了偏差调整估计量的渐近正态性。通过渐进均方误差比较了几位作者讨论的拟议估计量和其他估计量。从这些考虑，连同模拟结果，我们的加权方案的优点报告。
This paper is concerned with nonparametric binomial regression. A kernel-based binomial regression estimator and its bias-adjusted version are proposed, of which kernel is weighted by the inverse of a variance estimator of the observed proportion at each covariate. It is shown that the asymptotic normality of the bias-adjusted estimator holds under some regularity conditions. The proposed estimators and other estimators discussed by several authors are compared through their asymptotic MSEs. From these considerations, together with the simulation results, advantages of our weighting scheme are reported.