Scale Parameter Estimation of the Laplace Model Using Different Asymmetric Loss Functions

Sajid Ali, Muhammad Aslam, Nasir Abbas, Syed Mohsin Ali Kazmi

Abstract


In the last few decades, there has been an emergent interest in the construction of flexible parametric classes of probability  distributions in Bayesian as compared to Classical approach. In present study Bayesian Analysis of Laplace model using Inverted Gamma, Inverted Chi-Squared informative, Levy and Gumbel Type-II priors is discussed. The properties of posterior distribution, credible interval, highest posterior density region (HPDR) and Bayes Factor are discussed in current study. Bayes estimators are derived under squared error loss function (SELF), precautionary loss function, weighted squared error loss function and modified (quadratic) squared error loss function. Hyperparameters are determined through Empirical Bayes method. The estimates are also compared using the posterior risks (PRs) under the said loss functions. The priors and loss functions are compared using a real life data set.


Full Text: PDF DOI: 10.5539/ijsp.v1n1p105

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

International Journal of Statistics and Probability   ISSN 1927-7032(Print)   ISSN 1927-7040(Online)

Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the 'ccsenet.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.

----------------------------------------------------------------------------------------------------------------------------------------------------------------------

doaj_logo_new_120 proquest_logo_120images_120.