Open AccessParametric Bootstrap for Estimating Mean Square Error of Proportion in Small Area Estimation
Author(s) -
Anggun Permatasari,
Khairil Anwar Notodiputro,
_ Erfiani
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/ijsrset19613
Subject(s) - small area estimation , statistics , mean squared error , estimation , parametric statistics , mathematics , sample size determination , econometrics , estimator , management , economics
Small area estimation (SAE) is an important alternative method to obtain information in a small area when the sample size is small. In this paper, we proposed a parametric bootstrap method to estimate mean square error (MSE) of proportion based on area unit levels. The purpose of this research has been focused on applying the parametric bootstrap method to estimate MSE in SAE for zero inflated binomial models (SAE ZIB). The results showed that the bootstrap method produced a smaller MSE than the direct estimation, implying that the SAE ZIB performs better when compared to the direct estimation