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    Wyświetlanie 1-5 z 5
    Tytuł:
    Estimation of Parameters for Small Areas Using Hierarchical Bayes Method in the Case of Known Model Hyperparameters
    Autorzy:
    Kubacki, Jan
    Powiązania:
    https://bibliotekanauki.pl/articles/465703.pdf
    Data publikacji:
    2012
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Small area estimation
    hierarchical Bayes estimation WinBUGS
    Opis:
    In the paper the method of parameters estimation using hierarchical Bayes (HB) method in the case of known model hyperparameters for a priori conditionals was presented. This approach has some advantage in comparison with subjective model parameters selection because of more simulation stability and allows obtaining estimates that has more regular distribution. As an example the data about average per capita income from Polish Household Budget Survey for counties (NUTS4) and auxiliary variables from Polish Tax Register (POLTAX) were used. The computation was done using WinBUGS software and R-project environment with R2WinBUGS package, which control the simulations in WinBUGS, and coda package, which allows performing the analysis of simulation results. In the paper sample code in R-project that can be used as a pattern for further similar applications was also presented. The efficiency of hierarchical Bayes estimation with other small area methods was compared. Such comparison was done for HB and EBLUP techniques, for which some consistency related to the precision of estimates obtained using both techniques was achieved.
    Źródło:
    Statistics in Transition new series; 2012, 13, 2; 261-278
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    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
    Tytuł:
    An empirical study of hierarchical Bayes small area estimators using different priors for model variances
    Autorzy:
    You, Yong
    Powiązania:
    https://bibliotekanauki.pl/articles/19938261.pdf
    Data publikacji:
    2023-09-08
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    CPO
    flat prior
    inverse gamma prior
    relative error
    variance component
    Opis:
    In this paper, we study hierarchical Bayes (HB) estimators based on different priors for small area estimation. In particular, we use inverse gamma and flat priors for variance components in the HB small area models of You and Chapman (2006) and You (2021). We evaluate the HB estimators through a simulation study and real data analysis. Our results indicate that using the inverse gamma prior for the variance components in the HB models can be very effective.
    Źródło:
    Statistics in Transition new series; 2023, 24, 4; 169-178
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Two-Component Normal Mixture Alternative to the Fay-Herriot Model
    Autorzy:
    Chakraborty, Adrijo
    Datta, Gauri Sankar
    Mandal, Abhyuday
    Powiązania:
    https://bibliotekanauki.pl/articles/465632.pdf
    Data publikacji:
    2016
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    Hierarchical Bayes
    heavy-tail distribution
    non-informative priors
    robustness to outliers
    small area estimation
    Opis:
    This article considers a robust hierarchical Bayesian approach to deal with random effects of small area means when some of these effects assume extreme values, resulting in outliers. In the presence of outliers, the standard Fay-Herriot model, used for modeling area-level data, under normality assumptions of random effects may overestimate the random effects variance, thus providing less than ideal shrinkage towards the synthetic regression predictions and inhibiting the borrowing of information. Even a small number of substantive outliers of random effects results in a large estimate of the random effects variance in the Fay-Herriot model, thereby achieving little shrinkage to the synthetic part of the model or little reduction in the posterior variance associated with the regular Bayes estimator for any of the small areas. While the scale mixture of normal distributions with a known mixing distribution for the random effects has been found to be effective in the presence of outliers, the solution depends on the mixing distribution. As a possible alternative solution to the problem, a two-component normal mixture model has been proposed, based on non-informative priors on the model variance parameters, regression coefficients and the mixing probability. Data analysis and simulation studies based on real, simulated and synthetic data show an advantage of the proposed method over the standard Bayesian Fay-Herriot solution derived under normality of random effects.
    Źródło:
    Statistics in Transition new series; 2016, 17, 1; 67-90
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Comparison of Small Area Estimation Methods for Poverty Mapping
    Autorzy:
    Guadarrama, María
    Molina, Isabel
    Rao, J. N. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/465671.pdf
    Data publikacji:
    2016
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    area level model
    non-linear parameters
    empirical best estimator
    hierarchical Bayes
    poverty mapping
    unit level models
    Opis:
    We review main small area estimation methods for the estimation of general nonlinear parameters focusing on FGT family of poverty indicators introduced by Foster, Greer and Thorbecke (1984). In particular, we consider direct estimation, the Fay-Herriot area level model (Fay and Herriot, 1979), the method of Elbers, Lanjouw and Lanjouw (2003) used by the World Bank, the empirical Best/Bayes (EB) method of Molina and Rao (2010) and its extension, the Census EB, and finally the hierarchical Bayes proposal of Molina, Nandram and Rao (2014). We put ourselves in the point of view of a practitioner and discuss, as objectively as possible, the benefits and drawbacks of each method, illustrating some of them through simulation studies.
    Źródło:
    Statistics in Transition new series; 2016, 17, 1; 41-66
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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