The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the unc...The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.展开更多
针对传统假设中个体寿命独立同分布的不足,构建了贝叶斯Weibull共享异质性模型,提出了对寿命服从Weibull分布的产品,运用基于Gibbs抽样的马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法动态模拟出参数后验分布的马尔可夫链,在...针对传统假设中个体寿命独立同分布的不足,构建了贝叶斯Weibull共享异质性模型,提出了对寿命服从Weibull分布的产品,运用基于Gibbs抽样的马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法动态模拟出参数后验分布的马尔可夫链,在异质性因子的先验分布为Gamma分布时,给出随机截尾条件下,参数在Weibull共享异质性模型中的贝叶斯估计,提高了计算的精度。借助数据仿真说明了利用WinBUGS(Bayesianinference using Gibbs sampling)软件包进行建模分析的过程,证明了该模型在可靠性应用中的直观性与有效性。展开更多
文摘The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.
文摘针对传统假设中个体寿命独立同分布的不足,构建了贝叶斯Weibull共享异质性模型,提出了对寿命服从Weibull分布的产品,运用基于Gibbs抽样的马尔可夫链蒙特卡罗(Markov chain Monte Carlo,MCMC)方法动态模拟出参数后验分布的马尔可夫链,在异质性因子的先验分布为Gamma分布时,给出随机截尾条件下,参数在Weibull共享异质性模型中的贝叶斯估计,提高了计算的精度。借助数据仿真说明了利用WinBUGS(Bayesianinference using Gibbs sampling)软件包进行建模分析的过程,证明了该模型在可靠性应用中的直观性与有效性。
