empirical best prediction
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2019 ◽  
Vol 13 (2) ◽  
pp. 1166-1197
Author(s):  
Maria Francesca Marino ◽  
Maria Giovanna Ranalli ◽  
Nicola Salvati ◽  
Marco Alfò

2018 ◽  
Vol 34 (2) ◽  
pp. 523-542 ◽  
Author(s):  
Thomas Zimmermann ◽  
Ralf Thomas Münnich

Abstract The demand for reliable business statistics at disaggregated levels, such as industry classes, increased considerably in recent years. Owing to small sample sizes for some of the domains, design-based methods may not provide estimates with adequate precision. Hence, modelbased small area estimation techniques that increase the effective sample size by borrowing strength are needed. Business data are frequently characterised by skewed distributions, with a few large enterprises that account for the majority of the total for the variable of interest, for example turnover. Moreover, the relationship between the variable of interest and the auxiliary variables is often non-linear on the original scale. In many cases, a lognormal mixed model provides a reasonable approximation of this relationship. In this article, we extend the empirical best prediction (EBP) approach to compensate for informative sampling, by incorporating design information among the covariates via an augmented modelling approach. This gives rise to the EBP under the augmented model. We propose to select the augmenting variable based on a joint assessment of a measure of predictive accuracy and a check of the normality assumptions. Finally, we compare our approach with alternatives in a model-based simulation study under different informative sampling mechanisms.


2016 ◽  
Vol 32 (3) ◽  
pp. 661-692 ◽  
Author(s):  
Tomáš Hobza ◽  
Domingo Morales

Abstract The article applies unit-level logit mixed models to estimating small-area weighted sums of probabilities. The model parameters are estimated by the method of simulated moments (MSM). The empirical best predictor (EBP) of weighted sums of probabilities is calculated and compared with plug-in estimators. An approximation to the mean-squared error (MSE) of the EBP is derived and a bias-corrected MSE estimator is given and compared with parametric bootstrap alternatives. Some simulation experiments are carried out to study the empirical behavior of the model parameter MSM estimators, the EBP and plug-in estimators and the MSE estimators. An application to the estimation of poverty proportions in the counties of the region of Valencia, Spain, is given.


Test ◽  
2015 ◽  
Vol 25 (3) ◽  
pp. 548-569 ◽  
Author(s):  
Miguel Boubeta ◽  
María José Lombardía ◽  
Domingo Morales

2002 ◽  
Vol 30 (6) ◽  
pp. 1782-1810 ◽  
Author(s):  
Jiming Jiang ◽  
P. Lahiri ◽  
Shu-Mei Wan

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