φ-混合误差下线性EV模型中最小二乘估计的渐近性质  

The asymptotic properties of least square estimators in the linear errors-in-variables regression model withφ-mixing errors

作　　者：邓新 田春雨 葛梅梅 叶静 丁洋 吴燚 Deng Xin;Tian Chunyu;Ge Meimei;Ye Jing;Ding Yang;Wu Yi(School of Mathematical and Finance,Chuzhou University,Chuzhou 239000,China;China Electronics Technology Group Corporation No.58 Research Institute,Nanjing 210000,China;School of Big Data and Artificial Intelligence,Chizhou University,Chizhou 247000,China)

机构地区：[1]滁州学院数学与金融学院,安徽滁州239000 [2]中国电子科技集团公司第五十八研究所,江苏南京210000 [3]池州学院大数据与人工智能学院,安徽池州247000

出　　处：《中国科学技术大学学报》2021年第2期164-172,共9页JUSTC

基　　金：the Scientific Research Foundation Funded Project of Chuzhou University(2018qd01);the Natural Science Foundation of Anhui Province(1908085QA01).

摘　　要：本文主要研究φ-混合随机误差下的简单线性EV模型.借助于φ-混合序列的中心极限定理和Marcinkiewicz型强大数定律,在较弱的假设条件下,建立了未知参数最小二乘估计的渐近正态性.另外,利用φ-混合随机变量加权和的强收敛性,得到了该最小二乘估计的强相合性.最后,给出了相关理论结果的数值模拟.The simple linear errors-in-variables(EV)model withφ-mixing random errors was mainly studied.By using the central limit theorem and the Marcinkiewicz-type strong law of large numbers for theφ-mixing sequence,the asymptotic normality of the least square(LS)estimators for the unknown parameters were established under some mild conditions.In addition,based on the strong convergence for weighted sums ofφ-mixing random variables,the strong consistency of the LS estimators were obtained.Finally,the simulation study was provided to verify the validity of the theoretical results.

关 键 词：EV模型 渐近正态性 强相合性 最小二乘估计 Φ-混合序列 

分 类 号：O211.4[理学—概率论与数理统计]