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Recursive Residuals Partial Sums Method for Testing Model Validity in Modelling of Spatial Data

机译:用于测试空间数据建模的模型有效性的递归残余

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摘要

The main objective of this work is to model spatial observations using linear regression analysis defined on a compact experimental region. To check the validity of an assumed model, tests based on Kologorov-Smirnov and Cramer-von Mises functionals of the partial sums (CUSUM) of the recursive residuals of the observations are proposed. It is shown that the limit of the sequence of the CUSUM processes of the recursive residuals for triangular array of design points does not depend on the model. It is given by the set-indexed Brownian sheet when the model is true. The performance of the tests are also studied by deriving the non trivial limiting power functions of the tests when the model is not true. Their finite sample size behaviors are compared with those of the well-known asymptotic F test and are investigated by simulation. It is shown in this study that both Cramer-von Mises and F tests perform better than the Kolmogorov-Smirnov test. The application of the proposed method in a real data is also exhibited. The design under which the data has been collected is given by a regular lattice.
机译:这项工作的主要目的是使用在紧凑的实验区域上定义的线性回归分析来模拟空间观察。为了检查假定模型的有效性,提出了基于观察结果的递归残余的部分总和(CUSUM)的Kologorov-Smirnov和Cramer-Von Miss的测试。结果表明,用于三角形设计点的递归残差的CuSum过程的序列的极限不依赖于模型。当模型是真的时,它由设定索引的褐色表给出。当模型不正确时,还通过导出测试的非琐碎限制功率函数来研究测试的性能。将其有限的样本尺寸行为与众所周知的渐近F试验进行比较,并通过模拟研究。在这项研究中显示,克拉默 - vonmm和f测试都比Kolmogorov-smirnov测试更好。还展现了在实际数据中的应用程序应用。所收集数据的设计由常规格子给出。

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