Estimating the Parameters of a Robust Geographically Weighted Regression Model in Gross Regional Domestics Product in East Java

  • Bayutama Isnaini Department of Statistics, IPB University, Bogor, 16680, Indonesia
  • Utami Dyah Syafitri Department of Statistics, IPB University, Bogor, 16680, Indonesia
  • Muhammad Nur Aidi Department of Statistics, IPB University, Bogor, 16680, Indonesia
Keywords: Outliers, M-estimator RGWR, S-estimator RGWR

Abstract

Geographically weighted regression (GWR) is a regression parameter estimation method that accommodates location elements. Estimates of regression parameters have problems when there are outliers in the modelled data, including data based on location. This problem can be handled by a robust method of outliers, the robust GWR method (RGWR). M-estimator and S-estimator have high efficiency and high breakdown points. This study aimed to determine the best regression parameter estimation model on gross regional domestic product (GRDP) data in East Java Province in 2015, which is indicated to have various value based on the characteristic of regency/city. The city of Surabaya has very different characteristics from other regions and is detected as outliers based on a GWR model error plot, so RGWR with M-estimator and S-estimator are used. The mean absolute deviation (MAD) ​​show that the best model for data in this study is the RGWR with M-estimator.

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Published
2019-09-26
Section
Articles