Local Linear Estimation for Spatiotemporal Models Based on Least Absolute Deviation

作     者：Wang, Hongxia Lin, Jinguan Wang, Jinde 

作者机构：Southeast Univ Dept Math Nanjing 211189 Jiangsu Peoples R China Nanjing Univ Dept Math Nanjing 210008 Jiangsu Peoples R China 

出 版 物：《COMMUNICATIONS IN STATISTICS-THEORY AND METHODS》 (统计学通讯：理论与方法)

年 卷 期：2015年第44卷第7期

页      面：1508-1522页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Nation Science Foundation of China [11301073, 11171065, 11271067] Natural Science Foundation of Jiangsu Province [BK20141326] 

主　　题：Least absolute deviation estimation Least-squares method Local linear regression Mixing condition 

摘      要：When the data contain outliers or come from population with heavy-tailed distributions, which appear very often in spatiotemporal data, the estimation methods based on least-squares (L-2) method will not perform well. More robust estimation methods are required. In this article, we propose the local linear estimation for spatiotemporal models based on least absolute deviation (L-1) and drive the asymptotic distributions of the L-1-estimators under some mild conditions imposed on the spatiotemporal process. The simulation results for two examples, with outliers and heavy-tailed distribution, respectively, show that the L-1-estimators perform better than the L-2-estimators.