Open AccessROBUST SPATIAL REGRESSION MODEL ON ORIGINAL LOCAL GOVERNMENT REVENUE IN JAVA 2017
Author(s) -
Winda Chairani Mastuti,
Anik Djuraidah,
Erfiani Erfiani
Publication year - 2020
Publication title -
indonesian journal of statistics and applications
Language(s) - English
Resource type - Journals
ISSN - 2599-0802
DOI - 10.29244/ijsa.v4i1.573
Subject(s) - outlier , autoregressive model , statistics , regression analysis , econometrics , robust regression , estimator , weighting , least absolute deviations , linear regression , mathematics , regression , ordinary least squares , computer science , medicine , radiology
Spatial regression measures the relationship between response and explanatory variables in the regression model considering spatial effects. Detecting and accommodating outliers is an important step in the regression analysis. Several methods can detect outliers in spatial regression. One of these methods is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model. This research applies a robust spatial autoregressive (RSAR) model with S- estimator to the Original Local Government Revenue (OLGR) data. The RSAR model with the 4-nearest neighbor weighting matrix is the best model produced in this study. The coefficient of the RSAR model gives a more relevant result. Median absolute deviation (MdAD) and median absolute percentage error (MdAPE) values in the RSAR model with 4-nearest neighbor give smaller results than the SAR model.