Open AccessSimulation Study of Robust Geographically Weighted Empirical Best Linear Unbiased Predictor on Small Area Estimation
Author(s) -
Naima Rakhsyanda,
Kusman Sadik,
Indahwati Indahwati
Publication year - 2021
Publication title -
indonesian journal of statistics and applications
Language(s) - English
Resource type - Journals
ISSN - 2599-0802
DOI - 10.29244/ijsa.v5i1p50-60
Subject(s) - outlier , estimator , statistics , small area estimation , mathematics , mean squared error , best linear unbiased prediction , population , spatial analysis , sample size determination , computer science , artificial intelligence , selection (genetic algorithm) , demography , sociology
Small area estimation can be used to predict the population parameter with small sample sizes. For some cases, the population units that are close spatially may be more related than units that are further apart. The use of spatial information like geographic coordinates are studied in this research. Outlier contaminations can affect small area estimations. This study was conducted using simulation methods on generated data with six scenarios. The scenarios are the combination of spatial effects (spatial stationary and spatial non-stationary) with outlier contamination (no outlier, symmetric outliers, and non-symmetric outliers). The purpose of this study was to compare the geographically weighted empirical best linear unbiased predictor (GWEBLUP) and robust GWEBLUP (RGWEBLUP) with direct estimator, EBLUP, and REBLUP using simulation data. The performance of the predictors is evaluated using relative root mean squared error (RRMSE). The simulation results showed that geographically weighted predictors have the smallest RRMSE values for scenarios with spatial non-stationary, therefore offer a better prediction. For scenarios with outliers, robust predictors with smaller RRMSE values offer more efficiency than non-robust predictors.