Problem
Research questions and friction points this paper is trying to address.
robust estimation
generalized linear models
normal quantiles
probability integral transformation
Robust estimation in generalized linear models based on the normal quantiles of the probability integral transformation
🤖 AI Summary
本文提出了一种基于概率积分变换和正态分位数函数的广义线性模型鲁棒估计新方法，通过最小化参数大小的鲁棒度量来解决异常值影响问题。
📝 Abstract
A new approach to robust estimation in generalized linear models is introduced. The idea of the method is to first transform the responses applying the composition of the normal quantile function and the probability integral transformation. Then, using that the transformed responses should follow a standard normal distribution, find the values of the parameters that minimize a robust measure of their size. In practice an approximation of this transformation is used. The proposed estimators are studied theoretically for distributions that depend on a single parameter and through simulations and examples for the particular cases of Poisson and logistic regression.
Innovation
Methods, ideas, or system contributions that make the work stand out.
robust estimation
generalized linear models
normal quantiles
probability integral transformation
standard normal distribution
🔎 Similar Papers
No similar papers found.
