Research Article
Bayesian estimation and prediction for the generalized Lindley distribution under asymmetric loss function
Abstract
The paper develops the Bayesian estimation procedure for the generalized Lindley distribution under squared error and general entropy loss
functions in case of complete sample of observations. For obtaining the
Bayes estimates, both non-informative and informative priors are used.
Monte Carlo simulation is performed to compare the behaviour of the
proposed estimators with the maximum likelihood estimators in terms
of their estimated risks. Discussion is further extended to Bayesian
prediction problem based on an informative sample where an attempt
is made to derive the prediction intervals for future observations.
Numerical illustrations are provided based on a real data example.
functions in case of complete sample of observations. For obtaining the
Bayes estimates, both non-informative and informative priors are used.
Monte Carlo simulation is performed to compare the behaviour of the
proposed estimators with the maximum likelihood estimators in terms
of their estimated risks. Discussion is further extended to Bayesian
prediction problem based on an informative sample where an attempt
is made to derive the prediction intervals for future observations.
Numerical illustrations are provided based on a real data example.
Keywords
Generalized Lindley distribution Loss functions Bayes estimator HPD intervals Bayes prediction and MCMC methods
References
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There are 1 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Statistics |
| Authors | |
| Publication Date | August 1, 2014 |
| Published in Issue | Year 2014 Volume: 43 Issue: 4 |