Robust Estimation for Semi-Functional Linear Model with Autoregressive Errors

被引:0
|
作者
Yang, Bin [1 ,2 ]
Chen, Min [3 ,4 ]
Su, Tong [1 ]
Zhou, Jianjun [1 ]
机构
[1] Yunnan Univ, Yunnan Key Lab Stat Modeling & Data Anal, Kunming 650091, Peoples R China
[2] Kunming Univ Sci & Technol, City Coll, Kunming 650500, Peoples R China
[3] Shanxi Univ, Sch Math Sci, Taiyuan 030006, Peoples R China
[4] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
基金
中国国家自然科学基金;
关键词
autoregressive errors; heavy-tail distribution; outlier; robust estimation; semi-functional linear model; NONLINEAR-REGRESSION; SPLINE ESTIMATORS;
D O I
10.3390/math11020277
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页数:14
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