Loss function is one of the most topics in Bayesian analysis. The aim of this paper is to study the estimation of the shape parameter of Laplace distribution using Bayesian technique under a new loss function, which is a compound function of LINEX function. The Bayes estimator of the parameter is derived under the prior distribution of the parameter based on Gamma prior distribution. Furthermore, Monte Carlo statistical simulations illustrate that the Bayes estimators obtained under LINEX-based loss function is affected by the prior parameter and the value of the shape parameter of the LINEX-based loss function. But when the sample size is large, they have less influence on the estimation result.
Published in | International Journal of Data Science and Analysis (Volume 3, Issue 6) |
DOI | 10.11648/j.ijdsa.20170306.14 |
Page(s) | 85-89 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Laplace Distribution, Bayes Estimation, LINEX-Based Loss Function, Prior Distribution
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APA Style
Lanping Li. (2017). Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function. International Journal of Data Science and Analysis, 3(6), 85-89. https://doi.org/10.11648/j.ijdsa.20170306.14
ACS Style
Lanping Li. Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function. Int. J. Data Sci. Anal. 2017, 3(6), 85-89. doi: 10.11648/j.ijdsa.20170306.14
AMA Style
Lanping Li. Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function. Int J Data Sci Anal. 2017;3(6):85-89. doi: 10.11648/j.ijdsa.20170306.14
@article{10.11648/j.ijdsa.20170306.14, author = {Lanping Li}, title = {Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function}, journal = {International Journal of Data Science and Analysis}, volume = {3}, number = {6}, pages = {85-89}, doi = {10.11648/j.ijdsa.20170306.14}, url = {https://doi.org/10.11648/j.ijdsa.20170306.14}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20170306.14}, abstract = {Loss function is one of the most topics in Bayesian analysis. The aim of this paper is to study the estimation of the shape parameter of Laplace distribution using Bayesian technique under a new loss function, which is a compound function of LINEX function. The Bayes estimator of the parameter is derived under the prior distribution of the parameter based on Gamma prior distribution. Furthermore, Monte Carlo statistical simulations illustrate that the Bayes estimators obtained under LINEX-based loss function is affected by the prior parameter and the value of the shape parameter of the LINEX-based loss function. But when the sample size is large, they have less influence on the estimation result.}, year = {2017} }
TY - JOUR T1 - Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function AU - Lanping Li Y1 - 2017/12/26 PY - 2017 N1 - https://doi.org/10.11648/j.ijdsa.20170306.14 DO - 10.11648/j.ijdsa.20170306.14 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 85 EP - 89 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20170306.14 AB - Loss function is one of the most topics in Bayesian analysis. The aim of this paper is to study the estimation of the shape parameter of Laplace distribution using Bayesian technique under a new loss function, which is a compound function of LINEX function. The Bayes estimator of the parameter is derived under the prior distribution of the parameter based on Gamma prior distribution. Furthermore, Monte Carlo statistical simulations illustrate that the Bayes estimators obtained under LINEX-based loss function is affected by the prior parameter and the value of the shape parameter of the LINEX-based loss function. But when the sample size is large, they have less influence on the estimation result. VL - 3 IS - 6 ER -