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Penalty and related estimation strategies in the spatial error model

机译:空间误差模型中的罚分及相关估计策略

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Spatial autoregressive models are powerful tools in the analysis ofrndata sets from diverse scientific areas of research such as econometrics,rnplant species richness, cancer mortality rates, image processing,rnanalysis of the functional Magnetic Resonance Imaging (fMRI) data,rnand many more. An important class in the host of spatial autoregressivernmodels is the class of spatial error models in which spatiallyrnlagged error terms are assumed. In this paper, we propose efficientrnshrinkage and penalty estimators for the regression coefficients ofrnthe spatial error model. We carry out asymptotic as well as simulationrnanalyses to illustrate the gain in efficiency achieved by these newrnestimators. Furthermore, we apply the new methodology to housingrnprices data and provide a bootstrap approach to compute predictionrnerrors of the new estimators.
机译:空间自回归模型是分析各种科学领域的数据集的有力工具,这些数据集包括计量经济学,植物物种丰富性,癌症死亡率,图像处理,功能磁共振成像(fMRI)数据分析等等。在空间自回归模型中,一个重要的类别是空间误差模型,其中假定了空间滞后误差项。在本文中,我们为空间误差模型的回归系数提出了有效的收缩和惩罚估计。我们进行渐近线和模拟分析,以说明这些新型刺激器在效率上的提高。此外,我们将新方法应用于住房价格数据,并提供了一种引导方法来计算新估算器的预测误差。

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