Abstract

In this paper, we study the semi-functional partial linear regression for spatial data with considering a both parametric and nonparametric modeling. In this case we obtain the asymptotic normality of the parametric component, and probability convergence with rate of the nonparametric component under spatial dependency. Finally, the performance of the parametric and nonparametric estimators, for finite spatial sample sizes, are given by using simulated and real data with comparison to the nonparametric kernel regression (FNR) model by using cross-validation and k nearest neighbor methods.

中文翻译：

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功能空间相关性下半功能部分线性回归估计的渐近结果

摘要

在本文中，我们研究了同时考虑参数和非参数建模的空间数据半函数部分线性回归。在这种情况下，我们获得了参数分量的渐近正态性，以及在空间相关性下概率随非参数分量的比率收敛。最后，通过使用模拟和真实数据，并通过使用交叉验证和k最近邻方法与非参数内核回归（FNR）模型进行比较，给出了针对有限空间样本大小的参数和非参数估计量的性能。