Temat: loss distribution - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Interval shrinkage estimation of the parameter of exponential distribution in the presence of outliers under loss functions
Autorzy:: Nasiri, Parviz
Powiązania:: https://bibliotekanauki.pl/articles/2108120.pdf
Data publikacji:: 2022-09-14
Wydawca:: Główny Urząd Statystyczny
Tematy:: interval information
mean square error
shrinkage estimator
exponential distribution
uniform distribution
outliers
Linex loss function
Opis:: In this paper, we studied estimators based on an interval shrinkage with equal weights point shrinkage estimators for all individual target points θ¯ ∈ (θ0, θ1) for exponentially distributed observations in the presence of outliers drawn from a uniform distribution. Estimators obtained from both shrinkage and interval shrinkage were compared, showing that the estimators obtained via the interval shrinkage method perform better. Symmetric and asymmetric loss functions were also used to calculate the estimators. Finally, a numerical study and illustrative examples were provided to describe the results.
Źródło:: Statistics in Transition new series; 2022, 23, 3; 65-78
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

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

Artykuł

Skocz do pozycji: 3.

Tytuł:: Investigation of half-normal model using informative priors under Bayesian structure
Autorzy:: Khawar Kiani, Sania
Aslam, Muhammad
Bhatti, M. Ishaq
Powiązania:: https://bibliotekanauki.pl/articles/18105212.pdf
Data publikacji:: 2023-09-08
Wydawca:: Główny Urząd Statystyczny
Tematy:: informative prior
squared root inverted gamma distribution (SRIG)
Bayesian hypothesis testing
loss functions
Opis:: This paper considers properties of half-normal distribution using informative priors under the Bayesian criterion. It employs the squared root inverted gamma, Chi-square and Rayleigh distributions as the prior distribution to construct the Posterior distributions of the respective distributional parameters. Hyperparameters are elicited via prior predictive distribution. The properties of posterior distribution are studied, and their graphs are presented using a real data set. A comprehensive simulation scheme is conducted using informative priors. Bayes estimates are obtained using the loss functions (squared error loss function, modified loss function, quadratic loss function and Degroot loss function). Statistical inferences interval estimates and Bayesian hypothesis testing are presented to demonstrate the usefulness of the study.
Źródło:: Statistics in Transition new series; 2023, 24, 4; 19-36
1234-7655
Pojawia się w:: Statistics in Transition new series
Dostawca treści:: Biblioteka Nauki

Artykuł

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