Spatial nonstationary hierarchical Bayes estimation of small area proportions

2021 ◽  
pp. 1-20
Author(s):  
Priyanka Anjoy ◽  
Hukum Chandra
Keyword(s):  
Small Area ◽  
Hierarchical Bayes ◽  
Bayes Estimation

Adaptive Hierarchical Bayes Estimation of Small Area Proportions

2017 ◽  
Vol 69 (2) ◽  
pp. 150-164 ◽  
Author(s):  
Benmei Liu ◽  
Partha Lahiri
Keyword(s):  
Random Effects ◽  
Small Area ◽  
Probability Distributions ◽  
Random Effect ◽  
Real Data ◽  
Hierarchical Bayes ◽  
Bayes Estimation ◽  
Proposed Model ◽  
Exponential Power ◽  
Power Class

Unit-level logistic regression models with mixed effects have been used for estimating small area proportions in the literature. Normality is commonly assumed for the random effects. Nonetheless, real data often show significant departures from normality assumptions of the random effects. To reduce the risk of model misspecification, we propose an adaptive hierarchical Bayes estimation approach in which the distribution of the random effect is chosen adaptively from the exponential power class of probability distributions. The richness of the exponential power class ensures the robustness of our hierarchical Bayes approach against departure from normality. We demonstrate the robustness of our proposed model using both simulated and real data. The results suggest that the proposed model works reasonably well to incorporate potential kurtosis of the random effects distribution.

Robust hierarchical Bayes estimation of exchangeable means

1991 ◽  
Vol 19 (1) ◽  
pp. 39-56 ◽  
Author(s):  
Jean-François Angers ◽  
James O. Berger
Keyword(s):  
Hierarchical Bayes ◽  
Bayes Estimation
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