Robust estimation for spatial semiparametric varying coefficient partially linear regression
Robust estimation for spatial semiparametric varying coefficient partially linear regression
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DOI：
10.1007/s00362-014-0629-z
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发表时间：
2015-11
影响因子：
1.3
通讯作者：
Qingguo Tang
中科院分区：
文献类型：
--
作者：
Qingguo Tang
本文研究了空间数据的变系数部分线性回归问题。一个通用的公式是用来处理平均回归，中位数回归，分位数回归和稳健的平均回归在一个设置。通过对非参数系数函数的分段局部多项式逼近，得到了模型的参数估计。通过用参数的估计量代替模型中的参数，利用局部线性逼近，得到了未知系数函数的局部估计量，并建立了未知参数向量的估计量的渐近分布.同时也得到了未知系数函数在内部点和边界点的估计量的渐近分布。我们的程序的有限样本性质进行了研究，通过Monte Carlo模拟。一个真实的土壤空间数据的例子来说明我们提出的方法。
This paper considers a varying coefficient partially linear regression with spatial data. A general formulation is used to treat mean regression, median regression, quantile regression and robust mean regression in one setting. The parametric estimators of the model are obtained through piecewise local polynomial approximation of the nonparametric coefficient functions. The local estimators of unknown coefficient functions are obtained by replacing the parameters in model with their estimators and using local linear approximations.The asymptotic distribution of the estimator of the unknown parameter vector is established. The asymptotic distributions of the estimators of the unknown coefficient functions at both interior and boundary points are also derived. Finite sample properties of our procedures are studied through Monte Carlo simulations. A real data example about spatial soil data is used to illustrate our proposed methodology.