Open AccessEstimation of variance of random effect in small area model with Spatial Empirical Best Linear Unbiased Prediction (SEBLUP)
Author(s) -
Titin Siswantining,
M G Naima,
Saskya Mary Soemartojo
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 - 10.1088/1742-6596/1442/1/012032
Subject(s) - small area estimation , statistics , estimator , mathematics , best linear unbiased prediction , simple random sample , variance (accounting) , random effects model , population , spatial analysis , sampling (signal processing) , bias of an estimator , estimation , sample size determination , minimum variance unbiased estimator , computer science , artificial intelligence , meta analysis , filter (signal processing) , business , sociology , accounting , management , medicine , computer vision , selection (genetic algorithm) , demography , economics
Survey sampling is one of the sampling methods of an object to provide information estimation of population parameters that became the focus of research. One of the methods that used to estimate population parameters is direct estimation method. However, when the direct estimation is used it will cause a large standard error. To handle that problem in small area we add information about the same parameters in other small areas which has similar character, or the value of the variables that are related to the variables being observed, this method is known as the small area estimation (SAE). In this mini thesis, small area method that we use consider spatial correlation between area, spatial Empirical Best Linear Unbiased Prediction (EBLUP). The estimator of spatial EBLUP depends on the variance component and spatial correlation, but in practice they are unknown. Therefore, to get the spatial EBLUP estimator it is necessary to first estimate the variance of random effect and correlations between area. In this mini thesis we use maximum likelihood method and scoring algorithm to estimate the variance of random effect and correlations between area.