叶鹏, 周秀轻. 污染数据线性回归模型的最小一乘估计[J]. 应用概率统计, 2017, 33(3): 221-231. DOI: 10.3969/j.issn.1001-4268.2017.03.001
引用本文: 叶鹏, 周秀轻. 污染数据线性回归模型的最小一乘估计[J]. 应用概率统计, 2017, 33(3): 221-231. DOI: 10.3969/j.issn.1001-4268.2017.03.001
YE Peng, ZHOU XiuQing. LAD Estimation for Linear Regression Models with Contaminated Data[J]. Chinese Journal of Applied Probability and Statistics, 2017, 33(3): 221-231. DOI: 10.3969/j.issn.1001-4268.2017.03.001
Citation: YE Peng, ZHOU XiuQing. LAD Estimation for Linear Regression Models with Contaminated Data[J]. Chinese Journal of Applied Probability and Statistics, 2017, 33(3): 221-231. DOI: 10.3969/j.issn.1001-4268.2017.03.001

污染数据线性回归模型的最小一乘估计

LAD Estimation for Linear Regression Models with Contaminated Data

  • 摘要: 对于线性回归模型, 在因变量受到另一与之独立的随机变量序列的污染时, 基于最小一乘的方法给出模型参数的估计. 在一定条件下,证明了估计量的相合性和渐近正态性, 并使用模拟对估计方法的小样本性质进行了分析.模拟结果显示, 本文所提方法在小样本情况下表现良好.

     

    Abstract: In this paper, linear regression models with contaminated data are considered. Estimation methods for the regression parameters based on least absolute deviations (LAD) are proposed, and properties of consistency and asymptotic normality of the proposed method are proved under some regular conditions. Simulations are done to assess the properties of the method when sample size is small, and simulation results show that the methods works well.

     

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