Robust Estimation for Semi-Functional Linear Model with Autoregressive Errors
Robust Estimation for Semi-Functional Linear Model with Autoregressive Errors
复制标题
具有自回归误差的半函数线性模型的鲁棒估计
DOI：
10.3390/math11020277
复制
发表时间：
2023-01
期刊：
影响因子：
2.4
通讯作者：
Jianjun Zhou
中科院分区：
文献类型：
--
作者：
Bin Yang;Min Chen;Tong Su;Jianjun Zhou
众所周知，传统的函数回归模型主要是基于最小二乘法或似然法。这些方法通常依赖于一些强假设，如错误无关性和正规性，但这些假设并不总是得到满足。例如，响应变量可能包含异常值，并且误差项是顺序相关的。违反假设可能会对模型估计造成不利影响。为此，提出了一种半函数自回归线性模型的稳健估计方法。通过仿真研究和两个实际数据的分析，比较了该方法与最小二乘法的效率。结果表明，在随机误差服从重尾分布的情况下，该方法优于最小二乘法。
It is well-known that the traditional functional regression model is mainly based on the least square or likelihood method. These methods usually rely on some strong assumptions, such as error independence and normality, that are not always satisfied. For example, the response variable may contain outliers, and the error term is serially correlated. Violation of assumptions can result in unfavorable influences on model estimation. Therefore, a robust estimation procedure of a semi-functional linear model with autoregressive error is developed to solve this problem. We compare the efficiency of our procedure to the least square method through a simulation study and two real data analyses. The conclusion illustrates that the proposed method outperforms the least square method, providing random errors follow the heavy-tail distribution.