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*Hasbi Yasin orcid scopus publons  -  Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
Budi Warsito  -  Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
Arief Rachman Hakim  -  Department of Statistics, Faculty of Sciences and Mathematics, Diponegoro University, Indonesia
Rahmasari Nur Azizah  -  Data Science Institute, I-Biostat, Hasselt University Belgium, Belgium
Open Access Copyright (c) 2022 MEDIA STATISTIKA under

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The presence of outliers will affect the parameter estimation results and model accuracy. It also occurs in the spatial regression model, especially the Spatial Autoregressive (SAR) model. Spatial Autoregressive (SAR) is a regression model where spatial effects are attached to the dependent variable. Removing outliers in the analysis will eliminate the necessary information. Therefore, the solution offered is to modify the SAR model, especially by giving special treatment to observations that have potentially become outliers. This study develops to modeling the life expectancy data in Central Java Province using a modified spatial autoregressive model with the Mean-Shift Outlier Model (MSOM) approach. Outliers are detected using the MSOM method. Then the result is used as the basis for modifying the SAR model. This modification, in principle, will reduce or increase the average of the observed data indicated as outliers. The results show that the modified model can improve the model accuracy compared to the original SAR model. It can be proved by the increased coefficient of determination and decreasing the Akaike Information Criterion (AIC) value of the modified model. In addition, the modified model can improve the skewness and kurtosis values of the residuals getting closer to the Normal distribution.

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Keywords: Life Expectancy; MSOM; Outlier detection; SAR
Funding: DRPM BRIN-PDUPT under contract 225-92/UN7.6.1/PP/2021

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