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首页> 外文会议>International Society for Photogrammetry and Remote Sensing Commission Technical Commission Symposium >W-BASED VS LATENT VARIABLES SPATIAL AUTOREGRESSIVE MODELS: EVIDENCE FROM MONTE CARLO SIMULATIONS
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W-BASED VS LATENT VARIABLES SPATIAL AUTOREGRESSIVE MODELS: EVIDENCE FROM MONTE CARLO SIMULATIONS

机译：基于W的VS潜在变量空间自回归模型：来自Monte Carlo模拟的证据

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摘要

The paper evaluates by means of Monte Carlo simulations the estimator of the regression coefficient obtained by the classical W-based spatial autoregressive model and the structural equations model with latent variables (SEM) on the basis of data sets that contain two types of spatial dependence: spillover from (i) a hotspot and (iia) first order queen contiguity neighbors or (iib) inverse distance related neighbors. The classical models are either correctly specified or ignore (i), as is common in practice. SEM takes spatial dependence into account by means of a fixed number of nearest neighbors as well as the dependent variable in the hotspot weighted by inverse distance. The estimation results are analyzed in terms of bias and root mean squared error (RMSE) for different values of the spatial lag parameters, specifications of weights matrices and sample sizes. The simulation results show that compared to the misspecified models SEM frequently has smaller bias and RMSE and even outperforms the correctly specified models in many cases. These trends increase when the spatial lag parameter for spillover increases. The lead of SEM also increases by sample size. Finally, SEM is more stable in terms of both bias and RMSE over various dimensions.
机译：本文通过蒙特卡罗模拟通过基于经典的W基的空间自回归模型和具有潜在变量（SEM）获得的回归系数的估计器基于包含两种空间依赖性的数据集：溢出（i）一个热点和（IIa）一阶女王邻近邻居或（IIB）逆距离相关邻居。经典模型是正确指定或忽略（i），就像在实践中一样常见。通过固定数量的最近邻居以及通过逆距离加权的热点中的相关变量，SEM考虑了空间依赖。在空间滞后参数的不同值，权重矩阵和样本尺寸的规格，在偏置和根均方误差（RMSE）方面分析估计结果。仿真结果表明，与错过的模型SEM相比，SEM经常具有较小的偏差和RMSE，并且在许多情况下甚至优于正确指定的模型。当溢出的空间滞后参数增加时，这些趋势增加。 SEM的铅也增加了样本大小。最后，SEM在各种尺寸上的偏差和RMSE方面更稳定。

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