Small area estimation of the mean using non-parametric M-quantile regression: a comparison when a linear mixed model does not hold

2011 ◽  
Vol 81 (8) ◽  
pp. 945-964 ◽  
Author(s):  
N. Salvati ◽  
M. G. Ranalli ◽  
M. Pratesi
2020 ◽  
Vol 18 (1) ◽  
pp. 2-22
Author(s):  
Kusman Sadik ◽  
Rahma Anisa ◽  
Euis Aqmaliyah

The most commonly used method of small area estimation (SAE) is the empirical best linear unbiased prediction method based on a linear mixed model. However, it is not appropriate in the case of the zero-inflated target variable with a mixture of zeros and continuously distributed positive values. Therefore, various model-based SAE methods for zero-inflated data are developed, such as the Frequentist approach and the Bayesian approach. Both approaches are compared with the survey regression (SR) method which ignores the presence of zero-inflation in the data. The results show that the two SAE approaches for zero-inflated data are capable to yield more accurate area mean estimates than the SR method.


Author(s):  
María Dolores Esteban ◽  
María José Lombardía ◽  
Esther López-Vizcaíno ◽  
Domingo Morales ◽  
Agustín Pérez

Test ◽  
2018 ◽  
Vol 28 (2) ◽  
pp. 565-597 ◽  
Author(s):  
Monique Graf ◽  
J. Miguel Marín ◽  
Isabel Molina

2018 ◽  
Vol 86 (3) ◽  
pp. 541-570 ◽  
Author(s):  
Annamaria Bianchi ◽  
Enrico Fabrizi ◽  
Nicola Salvati ◽  
Nikos Tzavidis

2010 ◽  
Vol 10 (2) ◽  
pp. 215-239 ◽  
Author(s):  
W González-Manteiga ◽  
MJ Lombarda ◽  
I Molina ◽  
D Morales ◽  
L Santamaría

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