Small area estimation under a temporal bivariate area-level linear mixed model with independent time effects

2020 ◽  
Author(s):  
Roberto Benavent ◽  
Domingo Morales
Keyword(s):  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Small Area Estimation ◽  
Time Effects ◽  
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

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 with Zero-Inflated Data – a Simulation Study

2016 ◽  
Vol 32 (4) ◽  
pp. 963-986 ◽  
Author(s):  
Sabine Krieg ◽  
Harm Jan Boonstra ◽  
Marc Smeets
Keyword(s):  
Simulation Study ◽  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Real Data ◽  
Small Sample ◽  
Artificial Data ◽  
Normal Distributions ◽  
Area Estimation ◽  
Small Sample Sizes

Abstract Many target variables in official statistics follow a semicontinuous distribution with a mixture of zeros and continuously distributed positive values. Such variables are called zero inflated. When reliable estimates for subpopulations with small sample sizes are required, model-based small-area estimators can be used, which improve the accuracy of the estimates by borrowing information from other subpopulations. In this article, three small-area estimators are investigated. The first estimator is the EBLUP, which can be considered the most common small-area estimator and is based on a linear mixed model that assumes normal distributions. Therefore, the EBLUP is model misspecified in the case of zero-inflated variables. The other two small-area estimators are based on a model that takes zero inflation explicitly into account. Both the Bayesian and the frequentist approach are considered. These small-area estimators are compared with each other and with design-based estimation in a simulation study with zero-inflated target variables. Both a simulation with artificial data and a simulation with real data from the Dutch Household Budget Survey are carried out. It is found that the small-area estimators improve the accuracy compared to the design-based estimator. The amount of improvement strongly depends on the properties of the population and the subpopulations of interest.

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.

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