Small area estimation with spatio-temporal Fay–Herriot models

Marhuenda, Yolanda; Molina, Isabel; Morales, Domingo

doi:10.1016/j.csda.2012.09.002

Cited by 120 publications

(83 citation statements)

References 18 publications

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“…For instance, accurate statistical information concerning the well‐being of people at the regional level is needed to address policies or programs aimed at reducing poverty in poorer regions. The estimation of poverty at regional or local levels is then an important task for policy makers (Marhuenda, Molina & Morales, ). This is also true, among many other cases, when health agencies and policy makers need to know the spatial patterns of a rare disease (e.g., chronic disease or cancer) to identify small areas with high disease risk and implement preventative action.…”

Section: Discussionmentioning

confidence: 99%

Spatial generalized linear mixed models in small area estimation

Torabi

2019

Can J Statistics

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In survey sampling, policy decisions regarding the allocation of resources to sub‐groups of a population depend on reliable predictors of their underlying parameters. However, in some sub‐groups, called small areas due to small sample sizes relative to the population, the information needed for reliable estimation is typically not available. Consequently, data on a coarser scale are used to predict the characteristics of small areas. Mixed models are the primary tools in small area estimation (SAE) and also borrow information from alternative sources (e.g., previous surveys and administrative and census data sets). In many circumstances, small area predictors are associated with location. For instance, in the case of chronic disease or cancer, it is important for policy makers to understand spatial patterns of disease in order to determine small areas with high risk of disease and establish prevention strategies. The literature considering SAE with spatial random effects is sparse and mostly in the context of spatial linear mixed models. In this article, small area models are proposed for the class of spatial generalized linear mixed models to obtain small area predictors and corresponding second‐order unbiased mean squared prediction errors via Taylor expansion and a parametric bootstrap approach. The performance of the proposed approach is evaluated through simulation studies and application of the models to a real esophageal cancer data set from Minnesota, U.S.A. The Canadian Journal of Statistics 47: 426–437; 2019 © 2019 Statistical Society of Canada

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Section: Discussionmentioning

confidence: 99%

Spatial generalized linear mixed models in small area estimation

Torabi

2019

Can J Statistics

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“…We use data from the Spanish living conditions survey (SLCS) corresponding to the year 2008. A description of the used data appears in Esteban et al [24] and Marhuenda et al [25] The SLCS is the Spanish version of the European statistics on income and living conditions (EU-SILC), which is one of the statistical operations that have been harmonized for European Union countries. The D = 13 Spanish provinces from the two cited regions are the target domains.…”

Section: Estimation Of Poverty Proportionsmentioning

confidence: 99%

Tests for the variance parameter in the Fay–Herriot model

2015

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The Fay-Herriot model is a linear mixed model that plays a relevant role in small area estimation (SAE). Under the SAE set-up, tools for selecting an adequate model are required. Applied statisticians are often interested on deciding if it is worthwhile to use a mixed effect model instead of a simpler fixed-effect model. This problem is not standard because under the null hypothesis the random effect variance is on the boundary of the parameter space. The likelihood ratio test and the residual likelihood ratio test are proposed and their finite sample distributions are derived. Finally, we analyse their behaviour under simulated scenarios and we also apply them to real data.

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“…Rao and You (1994) and Esteban et al (2012) study longitudinal area-level models with time effects under assumption of the AR(1) model and independent area-level effects. In Marhuenda et al (2013), area-level model with AR(1) time effects and SAR(1) area effects is proposed. Singh et al (2005) using the Kalman filtering approach propose a spatiotemporal model.…”

Section: Introductionmentioning

confidence: 99%

On Prediction for Correlated Domains in Longitudinal Surveys

Żądło

2015

Communications in Statistics - Theory and Methods

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In the survey sampling estimation or prediction of both population's and subopulation's (domain's) characteristics is one of the key issues. In the case of the estimation or prediction of domain's characteristics one of the problems is looking for additional sources of information that can be used to increase the accuracy of estimators or predictors. One of these sources may be spatial and temporal autocorrelation. Due to the mean squared error (MSE) estimation, the standard assumption is that random variables are independent for population elements from different domains. If the assumption is taken into account, spatial correlation may be assumed only inside domains. In the paper, we assume some special case of the linear mixed model with two random components that obey assumptions of the first-order spatial autoregressive model SAR(1) (but inside groups of domains instead of domains) and first-order temporal autoregressive model AR(1). Based on the model, the empirical best linear unbiased predictor will be proposed together with an estimator of its MSE taking the spatial correlation between domains into account.

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Small area estimation with spatio-temporal Fay–Herriot models

Cited by 120 publications

References 18 publications

Spatial generalized linear mixed models in small area estimation

Spatial generalized linear mixed models in small area estimation

Tests for the variance parameter in the Fay–Herriot model

On Prediction for Correlated Domains in Longitudinal Surveys

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