Small area estimation: the EBLUP estimator based on spatially correlated random area effects

Pratesi, Monica; Salvati, Nicola

doi:10.1007/s10260-007-0061-9

Cited by 134 publications

(123 citation statements)

References 13 publications

Supporting

Mentioning

117

Contrasting

Unclassified

Order By: Relevance

“…Under the model (8) the Spatial EBLUP estimator is equal to (see for example formula (8) in Pratesi and Salvati (2008))…”

Section: Introductionmentioning

confidence: 99%

“…One of these techniques is the Spatial EBLUP (Spatial Empirical Best Linear Unbiased Prediction). It is usually based on the assumption that the spatial relationships between domains can be modelled by the simultaneous autoregressive process SAR (see Pratesi and Salvati (2008), p. 114 for better explanation of this term). The method was introduced by Cressie (1991) and is explained in detail in the publications of Saei and Chambers (2003), Pratesi and Salvati (2004, 2008, Singh et al (2005), Petrucci and Salvati (2006).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Small Area Estimation of Income under Spatial Sar Model

Kubacki¹,

Jędrzejczak²

2016

Statistics in Transition New Series

View full text Add to dashboard Cite

The paper presents the method of hierarchical Bayes (HB) estimation under small area models with spatially correlated random effects and a spatial structure implied by the Simultaneous Autoregressive (SAR) process. The idea was to improve the spatial EBLUP by incorporating the HB approach into the estimation algorithm. The computation procedure applied in the paper uses the concept of sampling from a posterior distribution under generalized linear mixed models implemented in WinBUGS software and adapts the idea of parameter estimation for small areas by means of the HB method in the case of known model hyperparameters. The illustration of the approach mentioned above was based on a real-world example concerning household income data. The precision of the direct estimators was determined using own three-stage procedure which employs Balanced Repeated Replication, bootstrap and Generalized Variance Function. Additional simulations were conducted to show the influence of the spatial autoregression coefficient on the estimation error reduction. The computations performed by 'sae' package for R project and a special procedure for WinBUGS reveal that the method provides reliable estimates of small area means. For high spatial correlation between domains, noticeable MSE reduction was observed, which seems more evident for HB-SAR method as compared with the traditional spatial EBLUP. In our opinion, the Gibbs sampler, revealing the simultaneous nature of processes, especially for random effects, can be a good starting point for the simulations based on stochastic SAR processes.

show abstract

“…Under the model (8) the Spatial EBLUP estimator is equal to (see for example formula (8) in Pratesi and Salvati (2008))…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Small Area Estimation of Income under Spatial Sar Model

Kubacki¹,

Jędrzejczak²

2016

Statistics in Transition New Series

View full text Add to dashboard Cite

show abstract

“…However, because of its simplicity and good empirical properties, it is used to estimate many other indicators, especially the poverty rate (Pratesi & Salvati 2008, Wawrowski 2014 It is assumed that sampling variance ψ is known, though in practice it is estimated. Likewise, random effect variance u 2 s also needs to be estimated.…”

Section: Fay-herriot Modelmentioning

confidence: 99%

An Evaluation of Company Performance Using the Fay-Herriot Model

Dehnel

Pietrzak

Wawrowski

2017

AOC

View full text Add to dashboard Cite

Information about monthly characteristics of the small business sector is currently provided mainly by sample surveys conducted, among others, by the Central Statistical Office. Sample size enables parameters of interest to be estimated with acceptable precision only at the country or voivodeship level or by NACE section. The growing demand for reliable estimates at a low level of aggregation is the motivating force behind research into the application of indirect methods of estimation based on auxiliary sources of information. Hence, the article seeks to evaluate the possibility of applying the Fay-Herriot model to estimate one of the basic economic variables that characterise small business, i.e. revenue, based on information collected in administrative registers maintained by the Ministry of Finance.

show abstract

“…In many papers small area predictors are derived under both area-level and unit-level models where the spatial correlation is taken into account but assuming that all data refer to single time point (Molina et al 2009;Petrucci and Salvati 2006;Petrucci et al 2005;Pratesi and Salvati 2008;Chandra et al 2007). The models are special cases of the Linear Mixed Model where one of the random components obeys the assumption of the SAR(1) process between subpopulations (what means that we assume the same realization of the random component for all of the population elements which belong to the same domain).…”

Section: Introductionmentioning

confidence: 99%

On longitudinal moving average model for prediction of subpopulation total

Żądło

2014

Stat Papers

View full text Add to dashboard Cite

In the paper the empirical best linear unbiased predictor of the subpopulation total is proposed under some longitudinal model where both temporal and spatial moving average models of profile specific random components are taken into account. Two estimators of the mean square error of the predictor are proposed as well. Considerations are supported by two Monte Carlo simulation studies and the case study.

show abstract

Small area estimation: the EBLUP estimator based on spatially correlated random area effects

Abstract: Small area estimation, Spatial correlation, SAR model, Spatial EBLUP, Lattice data,

Cited by 134 publications

References 13 publications

Small Area Estimation of Income under Spatial Sar Model

Small Area Estimation of Income under Spatial Sar Model

An Evaluation of Company Performance Using the Fay-Herriot Model

On longitudinal moving average model for prediction of subpopulation total

Contact Info

Product

Resources

About