Comparison of EBLUP and EBLUP Modification in Estimating Small Areas (Study : Percentages of Poverty in Bogor District)

Authors

  • Hary Merdeka  Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia
  • Kusman Sadik  Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia
  • Indahwati  Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia

DOI:

https://doi.org//10.32628/IJSRSET21841116

Keywords:

Small Area Estimation, EBLUP, Fixed-Effect, Random-Effect, EBLUP Modification, Percentage of Poverty

Abstract

A small area of the sample occurs when the sample size is very small. A large error will get if the parameters estimation is done with small the sample. One method to overcome it using a small area estimation (SAE) method. A small area estimator is a statistical technique to estimate the parameters of a sub-population with a small sample size. Estimates in the small area estimator method is based on the model and are indirect estimates. In this study the indirect method used is the EBLUP method and the modification of EBLUP estimator. The results of the alleged percentage of poverty in the Bogor district show that the EBLUP modification method is better compared to the expected method directly. This is based on the average of the RRMSE obtained.

References

  1. [BPS] Badan Pusat Statistik. 2013. Data dan informasi kemiskinan kabupaten/kota tahun 2013. Jakarta (ID): BPS.
  2. [BPS] Badan Pusat Statistik. 2016. Perhitungan dan analisis kemiskinan makro indonesia tahun 2016. Jakarta (ID): BPS.
  3. Anisa R, Kurnia A, Indahwati. 2014. Cluster information non-sampled area in small area estimation. IOSR Journal of Mathematics. 10(1): 15-19.
  4. Ferreti C, Molin, I. 2012. Fast EB for estimating complex poverty indicators in large populations. Journal of the Indian Society of Agricultural Statistics, 66 (1): 105 -120.
  5. Chandra H, Sud UC, Gharde Y. 2015. Small Area Estimation Using Estimated Population Level Auxiliary Data. J Communications in Statistics-Simulation and Computation, 44:5, 1197-1209. DOI: 10.1080/03610918. 2013.810255
  6. M.Herrador, M. D Esteban, T. Hobza, 2013, D. Morales. A Modified Nested-Error Regression Model for Small Area Estimation, Vol.47, No.2,258-273, http://dx.doi.org/
  7. Liu, H., Shah, S., Jiang, W. (2004), "On-line pencilan detection and data cleaning," Computers and Chemical Engineering, 28, 1635–1647.
  8. Menteiga-Gonzales. (2008) Bootstrap Mean Squared Error of a Small-Area EBLUP, J Communications in Statistics-Simulation and Computation, 78:5, 443-462. DOI: 10.1080/030949650601141811.
  9. McCulloch CE, Searle SR. 2001. Generalized, Linear and Mixed Models. New York: John Wiley & Sons, Inc.
  10. Namazi-Rad MR, Steel D. 2015. What Level of Statistical Model Should We Use in Small Area Estimation.Australian & New Zealand Journal of Statistics 57 (2) :275-298
  11. Rao JNK, Molina I. 2015. Small Area Estimation second edition. New York: Wiley.
  12. Sadik K. 2009. The best linear unbiased prediction method and hierarchical bayes for estimating small areas based on state space models [dissertation]. Bogor (ID): Institut Pertanian Bogor.
  13. V.Y Sundara, Sadik K, Kurnia A, Cluster information of non-sampled area in small area estimation of poverty indicators using Empirical Bayes. AIP Conference Proceedings 1827, 020026 (2017); doi: 10.1063/1.4979442
  14. Ybarra LMR, Lohr SL. 2008. Small Area Estimation when Auxiliary Information Measured with Error. Biometrika 95(4): 919-931

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2018-12-30

Issue

Volume 4, Issue 11, November-December-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Hary Merdeka, Kusman Sadik, Indahwati, " Comparison of EBLUP and EBLUP Modification in Estimating Small Areas (Study : Percentages of Poverty in Bogor District), International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 11, pp.160-168, November-December-2018. Available at doi : https://doi.org/10.32628/IJSRSET21841116