Binary response variables are commonly modeled by binomial models. The binomial overdispersion occurs if the
variability is greater than the variance of assumed model. The overdispersion can be caused by excess zeros. The overdispersion may
produce underestimated standard error which in turn will produce underestimated p-value. Therefore, Zero Inflated Binomial (ZIB)
models are considered to overcome the excess zeros in binomial data. A simulation study is employed to evaluate the performance of
models by using RRMSE and relative bias. The simulation showed that the proposed method SAE ZIB has better fit than SAE ZIB
Synthetic in terms of the smaller RRMSE. The proposed SAE ZIB method applies to unemployment data to estimate proportion of
unemployment in each district/regency during period of August 2016 In Jambi Province, Indonesia. The real data application showed
that SAE ZIB method is better than the direct estimates method in terms of the smaller standard error.

Published In:IJCSN Journal Volume 6, Issue 6

Date of Publication : December 2017

Pages : 746-752

Figures :03

Tables : 03

B. Hartono : master student in Department of Statistics, Bogor
Agricultural University. His main interests is on small area estimation.

Dr. A. Kurnia : ccurrently worked as a senior lecture in Department of
Statistics, Bogor Agricultural University. He is head in Department of
Statistics, Bogor Agricultural University. His main interests is on
statistical modellng and small area estimation.

Dr. Indahwati : ccurrently worked as a senior lecture in Department of
Statistics, Bogor Agricultural University. His main interests is on
statistical modellng, sampling design, and methodology.

Small Area Estimation, Zero Inflated Binomial, Unemployment.

Small area estimation with zero inflated binomial model
showed that the proposed method SAE ZIB (survey and
unsurvey units) has better fit than SAE ZIB Synthetic
(survey units only) in terms of the smaller RRMSE. The
appropriate initialization values and arrangement of
explanatory variables included in the models are
important things to overcome the failures in algorithms.
This model can be used to estimate proportion of
unemployments for each area as well as for each
district/regency in Jambi Province, Indonesia. The
application to unemployment data results that the
proportion estimate of SAE ZIB method is similar with
the current publication (4 unemployments of 100 labor
forces). Standard error of SAE ZIB method (0.0163) in
Jambi Province less than direct estimates method
(0.0164). It means the proposed SAE ZIB method is better
than the direct estimates method when applied to
unemployment data.

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