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Article

Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification

1
Institute for Mathematical Research, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
2
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi 0248, Nigeria
3
Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Jinyu Li
Symmetry 2021, 13(11), 2030; https://doi.org/10.3390/sym13112030
Received: 6 August 2021 / Revised: 7 October 2021 / Accepted: 22 October 2021 / Published: 27 October 2021
Influential observations (IOs), which are outliers in the x direction, y direction or both, remain a problem in the classical regression model fitting. Spatial regression models have a peculiar kind of outliers because they are local in nature. Spatial regression models are also not free from the effect of influential observations. Researchers have adapted some classical regression techniques to spatial models and obtained satisfactory results. However, masking or/and swamping remains a stumbling block for such methods. In this article, we obtain a measure of spatial Studentized prediction residuals that incorporate spatial information on the dependent variable and the residuals. We propose a robust spatial diagnostic plot to classify observations into regular observations, vertical outliers, good and bad leverage points using a classification based on spatial Studentized prediction residuals and spatial diagnostic potentials, which we refer to as ISRsPosi and ESRsPosi. Observations that fall into the vertical outliers and bad leverage points categories are referred to as IOs. Representations of some classical regression measures of diagnostic in general spatial models are presented. The commonly used diagnostic measure in spatial diagnostics, the Cook’s distance, is compared to some robust methods, Hi2 (using robust and non-robust measures), and our proposed ISRsPosi and ESRsPosi plots. Results of our simulation study and applications to real data showed that the Cook’s distance, non-robust Hsi12 and robust Hsi22 were not very successful in detecting IOs. The Hsi12 suffered from the masking effect, and the robust Hsi22 suffered from swamping in general spatial models. Interestingly, the results showed that the proposed ESRsPosi plot, followed by the ISRsPosi plot, was very successful in classifying observations into the correct groups, hence correctly detecting the real IOs. View Full-Text
Keywords: spatial regression model; influential observation; outlier; leverage; prediction residual; masking and swamping; diagnostic spatial regression model; influential observation; outlier; leverage; prediction residual; masking and swamping; diagnostic
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MDPI and ACS Style

Baba, A.M.; Midi, H.; Adam, M.B.; Abd Rahman, N.H. Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification. Symmetry 2021, 13, 2030. https://doi.org/10.3390/sym13112030

AMA Style

Baba AM, Midi H, Adam MB, Abd Rahman NH. Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification. Symmetry. 2021; 13(11):2030. https://doi.org/10.3390/sym13112030

Chicago/Turabian Style

Baba, Ali M., Habshah Midi, Mohd B. Adam, and Nur H. Abd Rahman. 2021. "Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification" Symmetry 13, no. 11: 2030. https://doi.org/10.3390/sym13112030

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