Outlier detection and accommodation in general spatial models

Dai, Xianhua; Jin, Libin; Shi, Anqi; Shi, Lei

doi:10.1007/s10260-015-0348-1

Cited by 18 publications

(18 citation statements)

References 32 publications

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“…There are 2 approaches at least to deal with them. First is accommodation using alternative models (or modified outlier model) to modeling the potential outliers or influential studies, which can greatly improve the model fitting. The second is to suggest a robust estimation method, which is not sensitive to the presence of outlier or influential observations.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Influence diagnostics in meta‐regression model

Shi

Zuo

et al. 2017

Research Synthesis Methods

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This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Influence diagnostics in meta‐regression model

Shi

Zuo

et al. 2017

Research Synthesis Methods

Self Cite

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“…Works on spatial regression diagnostic in the literature mainly focused on autocorrelation in the residuals, mostly using time series analogy [13][14][15]. Some remarkable works in the spatial regression model can be found in [17,18,20].…”

Section: = (mentioning

confidence: 99%

“…[13][14][15][16]. However, only scarce articles deal with the detection of IOs in spatial regression models, some examples which include [17][18][19][20]. Christensen et al [17] and Haining [18] adapted one of the diagnostic measures in [3], in detecting influential observations in spatial error autoregression model.…”

Section: Introductionmentioning

confidence: 99%

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

Baba¹,

Midi²,

Adam³

et al. 2021

Preprint

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Influential Observations, which are outliers in x direction, y direction or both, remain a hitch in classical regression model fitting. Spatial regression model, with peculiar nature of outliers due to their local nature, is not free from the effect of such influential observations. Researchers have adapted some classical regression techniques to the spatial models and yielded satisfactory results. However, masking or/and swamping remain stumbling block to such methods. We obtained the spatial representation of the classical regression measures of diagnostic in general spatial model. Commonly used diagnostic measure in spatial diagnostic, the Cook's distance, is compared to some robust methods, Hi2 (using robust and non-robust measures), and classification based on generalized residuals and diagnostic generalized potentials, ISRs-Posi and ESRs-Posi, with the help of the obtained spatial prediction residuals and the spatial leverage term. Results of simulation and applications to real data have shown the advantage of the ISRs-Posi and ESRs-Posi due to classification of outliers over Cook's distance and non-robust Hsi12, which suffer from masking, and robust Hsi22 which suffer from swamping in general spatial model.

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“…Other articles 2 of 20 in the literature deal with regressions with correlated residuals, e.g., [13][14][15][16][17]. However, only a few articles deal with the detection of IOs in spatial regression models; some examples include [18][19][20][21][22]. Some robust estimation methods in spatial regression are [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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 ISRs−Posi and ESRs−Posi. 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 ISRs−Posi and ESRs−Posi 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 ESRs−Posi plot, followed by the ISRs−Posi plot, was very successful in classifying observations into the correct groups, hence correctly detecting the real IOs.

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Outlier detection and accommodation in general spatial models

Cited by 18 publications

References 32 publications

Influence diagnostics in meta‐regression model

Influence diagnostics in meta‐regression model

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

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

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