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  • 标题:Bayesian Approach in Estimation of Scale Parameter of Nakagami Distribution
  • 本地全文:下载
  • 作者:Azam Zaka ; Ahmad Saeed Akhter
  • 期刊名称:Pakistan Journal of Statistics and Operation Research
  • 印刷版ISSN:2220-5810
  • 出版年度:2014
  • 卷号:10
  • 期号:2
  • 页码:217-228
  • DOI:10.1234/pjsor.v10i2.657
  • 语种:English
  • 出版社:College of Statistical and Actuarial Sciences
  • 摘要:Normal 0 false false false EN-US X-NONE X-NONE Nakagami distribution is a flexible life time distribution that may offer a good fit to some failure data sets. It has applications in attenuation of wireless signals traversing multiple paths, deriving unit hydrographs in hydrology, medical imaging studies etc. In this research, we obtain Bayesian estimators of the scale parameter of Nakagami distribution. For the posterior distribution of this parameter, we consider Uniform, Inverse Exponential and Levy priors. The three loss functions taken up are Squared Error Loss function, Quadratic Loss Function and Precautionary Loss function. The performance of an estimator is assessed on the basis of its relative posterior risk. Monte Carlo Simulations are used to compare the performance of the estimators. It is discovered that the PLF produces the least posterior risk when uniform priors is used. SELF is the best when inverse exponential and Levy Priors are used. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}
  • 关键词:Nakagami distribution, bayesian estimation, square error loss function, quadratic loss function, precautionary loss function.
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