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Nonparametric Identification of the Spatial Autoregression Model under A Priori Stochastic Uncertainty

机译：先验随机不确定性下空间自回归模型的非参数辨识

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摘要

For the process of spatial autoregression of the order (1,1), constructed were the locally most powerful sign criteria for verification of the hypotheses about the coefficients of the autoregression equation in the conditions where the distribution of the innovative process was unknown. The statistics of criteria are free of distribution; their asymptotic normality was proved. An algorithm to construct the point estimates of the coefficients of the autoregression equation was proposed on the basis of the statistics of the sign criteria. The assumptions about the innovative sequence of autoregression are rather weak and do not require finiteness of variance or evenness of density. All methods are stable to the observation "overshoots."
机译：对于阶数为（1,1）的空间自回归过程，构造了局部最强大的符号准则，用于验证在创新过程分布未知的条件下有关自回归方程系数的假设。标准的统计信息不予分发;证明了它们的渐近正态性。在符号标准的统计基础上，提出了一种构造自回归方程系数点估计的算法。关于自回归的创新序列的假设相当薄弱，不需要方差有限或密度均匀。所有方法对于观察到的“过冲”都是稳定的。

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