Robust Geographically Weighted Regression Modeling using Least Absolute Deviation and M-Estimator

Authors

  • Puteri Pekerti Wulandari  Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia
  • Anik Djuraidah  Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia
  • Aji Hamim Wigena  Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia

DOI:

https://doi.org/10.32628/IJSRSET196123

Keywords:

Geographically Weighted Regression, Least Absolute Deviation, M-Estimator

Abstract

Geographically weighted regression (GWR) is development of multiple regression that has spatial varying, so that the estimator of GWR is different for each location. Parameter estimation in GWR uses weighted least square method which is vulnerable to outlier and can cause biased parameter estimation. The robust GWR (RGWR) with LAD and M-estimator is resistance to outliers. This research estimated parameters on RGWR using LAD and M-estimator method and uses data of Java gross domestic product (GRDP) in 2015 containing several outliers. The result showed that RGWR model was better than GWR with M-estimator, and the predictions were closer to the actual values.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-02-28

Issue

Volume 6, Issue 1, January-February-2019

Section

Research Articles

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Puteri Pekerti Wulandari, Anik Djuraidah, Aji Hamim Wigena "Robust Geographically Weighted Regression Modeling using Least Absolute Deviation and M-Estimator" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 1, pp.238-245, January-February-2019. Available at doi : https://doi.org/10.32628/IJSRSET196123