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Wyświetlanie 1-2 z 2
Tytuł:
APPLICATION OF EBLUP ESTIMATION TO THE ANALYSIS OF SMALL AREAS ON THE BASIS OF POLISH HOUSEHOLD BUDGET SURVEY
Autorzy:
Jędrzejczak, Alina
Kubacki, Jan
Powiązania:
https://bibliotekanauki.pl/articles/453676.pdf
Data publikacji:
2009
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
small area estimation
empirical best linear unbiased predictor (EBLUP)
household budget survey
variance estimation
Opis:
In the paper the results of small area estimation using empirical best linear unbiased predictor (EBLUP) for the data coming from Polish Household Budget Survey are presented. The results were obtained using small area models of household expenditures for regions. Estimation of sampling errors was conducted by means of the balanced repeated replication (BRR) technique. The estimation of EBLUPs and their corresponding mean square errors (MSE) was carried out using variance components technique. To calculate MSE of EBLUP the maximum likelihood method (ML) and restricted maximum likelihood method (REML) were used. The computation was made using SAE package designed for R-project.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2009, 10, 1; 121-130
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Small Area Estimation of Income Under Spatial SAR Model
Autorzy:
Kubacki, Jan
Jędrzejczak, Alina
Powiązania:
https://bibliotekanauki.pl/articles/465667.pdf
Data publikacji:
2016
Wydawca:
Główny Urząd Statystyczny
Tematy:
small area estimation (SAE)
SAR model
hierarchical Bayes estimation
spatial empirical best linear unbiased predictor
Opis:
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.
Źródło:
Statistics in Transition new series; 2016, 17, 3; 365-390
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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