The performance of empirical bayes estimates on bootstrap method for poisson gamma models
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Date
2010Author
Sofura, Nia
Notodiputro, Khairil Anwar
Rahman, La Ode Abdul
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Negative MSE values occured in previous research conducted by Nadhiroh (2009). These
might happened due to the nature of jackknife estimators. Jackknife estimators can be negative
values under certain scenarios (Bell 2001). The alternative method to estimate the MSE value is
required, and this paper proposes bootstrap as an altternative method. To study the consistency of
Mean Square Error (MSE) and Average Relative Bias (ARB) on empirical bayes estimators, 2000
bootstrap samples (B) were generated. With B=500 MSE and ARB values seem to achieve
consistency and all of MSE values are non negative.
This research demonstrated that the Zero Inflated Negative Binomial (ZINB) method is
effective in the condition where the excess of zero occurs in small areas. It has been shown that
when the probability of zero increases, the smaller MSE values are produced. This research
showed that the probabilities of zero affected the ARB means significantly on α=0.05, whereas the
number of areas did not effect the ARB means significantly. Although the interaction between
probability of zero and number of areas is significant on α=0.05, the mean of MSE decreases as
the probability of zero increases with the rate of change the MSE means are different dependent
on number of small areas.