首页> 外文期刊>Journal of statistical computation and simulation >Bayesian estimation of renewal function for inverse Gaussian renewal process
【24h】

Bayesian estimation of renewal function for inverse Gaussian renewal process

机译:高斯逆更新过程的更新函数的贝叶斯估计

获取原文
获取原文并翻译 | 示例

摘要

Two approximation methods are used to obtain the Bayes estimate for the renewal function of inverse Gaussian renewal process. Both approximations use a gamma-type conditional prior for the location parameter, a non-informative marginal prior for the shape parameter, and a squared error loss function. Simulations compare the accuracy of the estimators and indicate that the Tieney and Kadane (T-K)-based estimator out performs Maximum Likelihood (ML)- and Lindley (L)-based estimator. Computations for the T-K-based Bayes estimate employ the generalized Newton's method as well as a recent modified Newton's method with cubic convergence to maximize modified likelihood functions. The program is available from the author.
机译:使用两种近似方法获得逆高斯更新过程的更新函数的贝叶斯估计。两种近似都对位置参数使用伽玛类型的条件先验,对形状参数使用非信息性边际先验,以及平方误差损失函数。仿真比较了估算器的准确性,并表明基于Tieney和Kadane(T-K)的估算器执行了基于最大似然(ML)和Lindley(L)的估算器。基于T-K的贝叶斯估计的计算使用广义牛顿法以及具有三次收敛性的最新改进牛顿法,以使修改后的似然函数最大化。该程序可从作者处获得。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号