In this present work,we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy di...In this present work,we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy distribution.These estimates have been obtained using gamma priors based on various loss functions such as squared error,entropy,weighted balance,and minimum expected loss functions.An investigation is carried out using Monte Carlo simulation to evaluate the effectiveness of the suggested estimators.The simulation provides a quantitative assessment of the estimates accuracy and efficiency under various conditions by comparing them in terms of mean squared error.Additionally,the monthly water capacity of the Shasta reservoir is examined to offer real-world examples of how the suggested estimations may be used and performed.展开更多
将硬判决融合协同频谱感知描述为贝叶斯二元假设检验问题,本文考虑感知信息传输错误的可能性,以最小化平均判决风险(贝叶斯风险)为目标的最优本地判决和最优判决融合可分别归结为LRT(likelihood ratio test)问题,并证明基于能量检测的本...将硬判决融合协同频谱感知描述为贝叶斯二元假设检验问题,本文考虑感知信息传输错误的可能性,以最小化平均判决风险(贝叶斯风险)为目标的最优本地判决和最优判决融合可分别归结为LRT(likelihood ratio test)问题,并证明基于能量检测的本地LRT与观测量的门限判决等价。当仅有本地判决结果可用时,融合中心通常假设本地观测量独立同分布,可证明此时的最优融合准则为N中取K的投票准则,并给出一种低复杂度的数值迭代算法来求解最优本地判决门限和投票融合门限。数值结果显示,大多数情况下最优N中取K的投票融合可被多数逻辑融合代替而几乎不增加判决风险。展开更多
文章主要研究了安徽省某气象台站年平均气温、月平均气温的结构性变化情况。该文在对气温数据进行正态性检验的基础上,运用ASAMC(annealing stochastic approximation Monte Carlo)算法对气温数据进行统计分析,估计出平均气温结构性变...文章主要研究了安徽省某气象台站年平均气温、月平均气温的结构性变化情况。该文在对气温数据进行正态性检验的基础上,运用ASAMC(annealing stochastic approximation Monte Carlo)算法对气温数据进行统计分析,估计出平均气温结构性变化的位置,并探索发生结构性变化的气象因素和非气象因素。展开更多
基金funded by Researchers Supporting Project number(RSPD2025R969),King Saud University,Riyadh,Saudi Arabia.
文摘In this present work,we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy distribution.These estimates have been obtained using gamma priors based on various loss functions such as squared error,entropy,weighted balance,and minimum expected loss functions.An investigation is carried out using Monte Carlo simulation to evaluate the effectiveness of the suggested estimators.The simulation provides a quantitative assessment of the estimates accuracy and efficiency under various conditions by comparing them in terms of mean squared error.Additionally,the monthly water capacity of the Shasta reservoir is examined to offer real-world examples of how the suggested estimations may be used and performed.
文摘将硬判决融合协同频谱感知描述为贝叶斯二元假设检验问题,本文考虑感知信息传输错误的可能性,以最小化平均判决风险(贝叶斯风险)为目标的最优本地判决和最优判决融合可分别归结为LRT(likelihood ratio test)问题,并证明基于能量检测的本地LRT与观测量的门限判决等价。当仅有本地判决结果可用时,融合中心通常假设本地观测量独立同分布,可证明此时的最优融合准则为N中取K的投票准则,并给出一种低复杂度的数值迭代算法来求解最优本地判决门限和投票融合门限。数值结果显示,大多数情况下最优N中取K的投票融合可被多数逻辑融合代替而几乎不增加判决风险。
文摘文章主要研究了安徽省某气象台站年平均气温、月平均气温的结构性变化情况。该文在对气温数据进行正态性检验的基础上,运用ASAMC(annealing stochastic approximation Monte Carlo)算法对气温数据进行统计分析,估计出平均气温结构性变化的位置,并探索发生结构性变化的气象因素和非气象因素。
