A Comparison of Cluster Method and Nearest Neighbor Method for Non-sample Area in the Small Area Estimation

Authors(3) :-Annastasia Nika Susanti, Kusman Sadik, Anang Kurnia

Small area estimation is an indirect method to estimate the parameter of a population by using the model approach. The problem that often arises in the small area estimation is non-sampled area then the area random effect of non-sampled areas is can not estimate because no sample units are available in these areas. This paper proposed a method to solve the non-sampled area problem by adding the cluster information and by using the nearest neighbor area to estimate the area random effect through the Fast Hierarchical Bayes (FHB) approach. These methods are compared by using the simulation study and the evaluation is based on the Absolute Relative Bias (ARB) and Relative Root Mean Square Error (RRMSE). The result shows that the estimation by using the cluster method has smaller ARB and RRMSE values than the estimation by using the nearest neighbor area in various sample sizes and various population sizes. Then it can be said that cluster method is better to provide the estimators of non-sampled area than the nearest neighbor area method.

Authors and Affiliations

Annastasia Nika Susanti
Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia
Kusman Sadik
Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia
Anang Kurnia
Department of Statistics, Bogor Agricultural University, Bogor, West Java, Indonesia

Small Area Estimation, Non-Sampled Area, Cluster Method, The Nearest Neighbor Area, Poverty Indicators.

  1. Kurnia A. 2009.”An empirical best prediction method for logarithmic transformation model in small area estimation with particular application to susenas data”.[doctoral dissertation] Bogor Agricultural University, Indonesia.
  2. Ghos M and Rao JNK. 1994.”Small Area Estimation : An Appraisal”. Statistical Science. 9(1): 55 -76 DOI:10.1214/ss/1177010647
  3. Molina I, Nandram B, Rao JNK. 2014. “Small area estimation of general parameters with application to poverty indicators: a hierarchical Bayes approach”. The Annals of Applied Statistics. 8 (2): 852-885 DOI: 10.1214/13-AOAS702
  4. Anisa R, Kurnia A, and Indahwati. 2014. “Cluster Information of Non-sampled Area in Small Area Estimation. IOSR Journal of Mathematics (IOSR-JM).10(1): 15-19.
  5. Wahyudi, Notodiputro KA, Kurnia A, and Anisa R. 2016. “A Study of Area Clustering using Factor Analysis in Small Area Estimation (An Analysis of Per Capita Expenditures of Subdistricts Level in Regency and Municipality of Bogor)”. AIP Conference Proceedings.1707(1). DOI : 10.1063/1.4940874
  6. Sundara VY, Sadik K, and Kurnia A. 2017. “Cluster Information of Non-sampled Area in Small Area Estimation of Poverty Indicators using Empirical Bayes”, AIP Conference Proceedings. 1827(1). DOI : 10.1063/1.4979442
  7. Molina I and Rao JNK. 2010. ”Small Area Estimation of Poverty Indicators”.The Canadian Journal of Statistics. 38(3) : 369 – 385
  8. Ferreti C and Molina I. 2012. “Fast EB Method for Estimating Complex Poverty Indicators in Large Population”. Journal of The Indian Society of Agricultural Statistics. 66(1) :105 -120.

Publication Details

Published in : Volume 4 | Issue 9 | July-August 2018
Date of Publication : 2018-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 463-468
Manuscript Number : IJSRSET1849101
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Annastasia Nika Susanti, Kusman Sadik, Anang Kurnia, " A Comparison of Cluster Method and Nearest Neighbor Method for Non-sample Area in the Small Area Estimation, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.463-468, July-August-2018. Citation Detection and Elimination     |     
Journal URL : https://ijsrset.com/IJSRSET1849101

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