ROBUST SPATIAL AUTOREGRESSIVE UNTUK PEMODELAN ANGKA HARAPAN HIDUP PROVINSI JAWA TIMUR

*Hidayatul Musyarofah  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Hasbi Yasin  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Tarno Tarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Published: 28 Feb 2020.
Open Access Copyright 2020 Jurnal Gaussian
License URL: http://creativecommons.org/licenses/by-nc-sa/4.0

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Abstract

Spatial regression analysis is regression method used for type of data has a spatial effect. Spatial regression showing the presence of spatial effects on the response variable (Y) is a Spatial Autoregressive (SAR). Outlier often found in research spatial data. The outlier is called the spatial outliers. The analysis can be used to handle outliers in general is Robust Regression. There are several estimator that can be used in which the estimator Robust Regression S, M, MM and LTS. Meanwhile, Robust Regression were used to handle spatial outlier is a combination of SAR and Regression Robust method to form a new method that is Robust Spatial Autoregressive (Robust SAR). Type estimator used in this study is the S-Estimator. This study was conducted to determine the best model on a case study Life Expectancy of East Java Province. The best model is analyzed by comparing the methods of SAR and SAR Robust method. Based on the analysis results obtained MSE and Adjusted R2 values for the SAR method are 1.7521 and 55.54% while for the Robust SAR method are 0.7456 and 62.30%. The Robust SAR model has a lower MSE value and a higher Adjusted R2 when compared to the SAR model. Thus the best model for modeling the life expectancy in East Java is Robust SAR models.


Keywords:Spatial Autoregressive (SAR), Robust SAR, Life expectancy

Keywords: Spatial Autoregressive (SAR), Robust SAR, Life expectancy

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