A generalized mixed model for skewed distributions applied to small area estimation

Test ◽  
2018 ◽  
Vol 28 (2) ◽  
pp. 565-597 ◽  
Author(s):  
Monique Graf ◽  
J. Miguel Marín ◽  
Isabel Molina
Keyword(s):  
Small Area ◽  
Mixed Model ◽  
Small Area Estimation ◽  
Skewed Distributions ◽  
Area Estimation

scholarly journals Small Area Estimation on Zero-Inflated Data Using Frequentist and Bayesian Approach

2020 ◽  
Vol 18 (1) ◽  
pp. 2-22
Author(s):  
Kusman Sadik ◽  
Rahma Anisa ◽  
Euis Aqmaliyah
Keyword(s):  
Bayesian Approach ◽  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Small Area Estimation ◽  
Prediction Method ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Best Linear Unbiased ◽  
Sr Method

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.

Small area estimation of expenditure means and ratios under a unit-level bivariate linear mixed model

2020 ◽  
pp. 1-26
Author(s):  
María Dolores Esteban ◽  
María José Lombardía ◽  
Esther López-Vizcaíno ◽  
Domingo Morales ◽  
Agustín Pérez
Keyword(s):  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Unit Level

scholarly journals Shrinkage Small Area Estimation Using a Semiparametric Mixed Model

2014 ◽  
Vol 27 (4) ◽  
pp. 605-617
Author(s):  
Seok-Oh Jeong ◽  
Manho Choo ◽  
Key-Il Shin
Keyword(s):  
Small Area ◽  
Mixed Model ◽  
Small Area Estimation ◽  
Area Estimation

scholarly journals Small Area Estimation with a Lognormal Mixed Model under Informative Sampling

2018 ◽  
Vol 34 (2) ◽  
pp. 523-542 ◽  
Author(s):  
Thomas Zimmermann ◽  
Ralf Thomas Münnich
Keyword(s):  
Small Area ◽  
Mixed Model ◽  
Predictive Accuracy ◽  
Small Area Estimation ◽  
Small Sample ◽  
Effective Sample Size ◽  
Area Estimation ◽  
Empirical Best Prediction ◽  
Informative Sampling ◽  
Small Sample Sizes

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.

Sae: A Stata Package For Unit Level Small Area Estimation

2018 ◽  
Author(s):  
Minh Cong Nguyen ◽  
Paul Corral ◽  
Joao Pedro Azevedo ◽  
Qinghua Zhao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Unit Level
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