Open AccessLive Expectancy Modelling using Spatial Durbin Robust Model
Author(s) -
Arief Rachman Hakim,
Budi Warsito,
Hasbi Yasin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1655/1/012098
Subject(s) - outlier , statistics , regression analysis , estimator , spatial analysis , robust regression , mathematics , regression , linear regression , econometrics
Spatial regression model is a model used to determine the relationship between response variables and predictor variables that have spatial influence in them. If the two variables have spatial influence, then the model that will be formed is the Spatial Durbin Model. One of the causes of the inaccuracy of the spatial regression model in predicting is observations of outliers. Removing outliers in spatial analysis can change the composition of spatial effects on the data. One method of settlement due to outliers in the spatial regression model is to use robust spatial regression. The application of the M-estimator parameter estimator principle is done in estimating the coefficient of spatial regression parameters that are robust to outliers. The results of modelling by applying the principle of M-estimator estimator on estimating the robust Spatial Durbin Model regression parameters are expected to be able to accommodate the existence of outliers in the spatial regression model. One example of the application of the Spatial Durbin Model Robust is the case of life expectancy modelling.