Outlier detection and accommodation in general spatial models

Xiaowen Dai, Libin Jin, Anqi Shi and Lei Shi ()
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Xiaowen Dai: Renmin University of China
Libin Jin: Renmin University of China
Anqi Shi: University of Wisconsin-Madison
Lei Shi: Yunnan University of Finance and Economics

Statistical Methods & Applications, 2016, vol. 25, issue 3, No 7, 453-475

Abstract: Abstract This paper studies outlier detection and accommodation in general spatial models including spatial autoregressive models and spatial error model as special cases. Using mean-shift and variance-weight models respectively, test statistics for multiple outliers are derived and the detecting procedures are proposed. In addition, several key diagnostic measures such as standardized residuals and leverage measure are defined in general spatial models. Outlier modified models are proposed to accommodate outliers in the data set. The performance of test statistics, including size and power, are examined via simulation studies. Three real examples are analyzed and the results show that the proposed methodology is useful for identifying and accommodating outliers in general spatial models.

Keywords: General spatial models; Outliers; Mean-shift outlier model; Variance-weight model; Score test; Outlier accommodation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10260-015-0348-1

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