Temat: informative priors - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A study on exponentiated Gompertz distribution under Bayesian discipline using informative priors
Autorzy:: Aslam, Muhammad
Afzaal, Mehreen
Bhatti, M. Ishaq
Powiązania:: https://bibliotekanauki.pl/articles/1917073.pdf
Data publikacji:: 2021-12-08
Wydawca:: Główny Urząd Statystyczny
Tematy:: exponentiated Gompertz distribution
loss functions
informative priors
Bayes estimators
posterior risks
Opis:: The exponentiated Gompertz (EGZ) distribution has been recently used in almost all areas of human endeavours, starting from modelling lifetime data to cancer treatment. Various applications and properties of the EGZ distribution are provided by Anis and De (2020). This paper explores the important properties of the EGZ distribution under Bayesian discipline using two informative priors: the Gamma Prior (GP) and the Inverse Levy Prior (ILP). This is done in the framework of five selected loss functions. The findings show that the two best loss functions are the Weighted Balance Loss Function (WBLF) and the Quadratic Loss Function (QLF). The usefulness of the model is illustrated by the use of reallife data in relation to simulated data. The empirical results of the comparison are presented through a graphical illustration of the posterior distributions.
Źródło:: Statistics in Transition new series; 2021, 22, 4; 101-119
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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

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