Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation,i.e.,the moisture recycling ratio,but various isotope-based models usually lead to different results,...Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation,i.e.,the moisture recycling ratio,but various isotope-based models usually lead to different results,which affects the accuracy of local moisture recycling.In this study,a total of 18 stations from four typical areas in China were selected to compare the performance of isotope-based linear and Bayesian mixing models and to determine local moisture recycling ratio.Among the three vapor sources including advection,transpiration,and surface evaporation,the advection vapor usually played a dominant role,and the contribution of surface evaporation was less than that of transpiration.When the abnormal values were ignored,the arithmetic averages of differences between isotope-based linear and the Bayesian mixing models were 0.9%for transpiration,0.2%for surface evaporation,and–1.1%for advection,respectively,and the medians were 0.5%,0.2%,and–0.8%,respectively.The importance of transpiration was slightly less for most cases when the Bayesian mixing model was applied,and the contribution of advection was relatively larger.The Bayesian mixing model was found to perform better in determining an efficient solution since linear model sometimes resulted in negative contribution ratios.Sensitivity test with two isotope scenarios indicated that the Bayesian model had a relatively low sensitivity to the changes in isotope input,and it was important to accurately estimate the isotopes in precipitation vapor.Generally,the Bayesian mixing model should be recommended instead of a linear model.The findings are useful for understanding the performance of isotope-based linear and Bayesian mixing models under various climate backgrounds.展开更多
A Bayesian analysis of the minimal model was proposed where both glucose and insulin were analyzed simultaneously under the insulin-modified intravenous glucose tolerance test (IVGTT). The resulting model was implemen...A Bayesian analysis of the minimal model was proposed where both glucose and insulin were analyzed simultaneously under the insulin-modified intravenous glucose tolerance test (IVGTT). The resulting model was implemented with a nonlinear mixed-effects modeling setup using ordinary differential equations (ODEs), which leads to precise estimation of population parameters by separating the inter- and intra-individual variability. The results indicated that the Bayesian method applied to the glucose-insulin minimal model provided a satisfactory solution with accurate parameter estimates which were numerically stable since the Bayesian method did not require approximation by linearization.展开更多
It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparame...It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure.展开更多
[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同...[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同分位数下(=0.10,0.25,0.50,0.75)分别建立线性分位数混合模型拟合E2水平的动态变化,并通过赤池信息量准则(akaike information criterion,AIC)与贝叶斯信息准则(Bayesian information criteria,BIC)从中选择最优模型作为联合模型的纵向子模型。其次,基于扩展的Cox比例风险模型建立生存子模型;进一步通过共享随机效应建立纵向与生存数据的贝叶斯分位数联合模型,并通过马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法估计其关联系数()。[结果]最优子模型筛选结果显示,=0.50时,纵向子模型的AIC与BIC值最小。在=0.50下构建贝叶斯分位数联合模型。联合模型结果显示,E2水平的动态变化与乳腺癌患者的生存结局显著性相关(=0.59,HR=1.80,95%CI:1.47~2.24)。新辅助治疗是乳腺癌患者的保护因素(HR=0.155,95%CI:0.047~0.384),能够降低乳腺癌患者84.5%死亡风险。[结论]乳腺癌患者E2水平增加与不良生存预后相关,新辅助治疗可降低乳腺癌患者的死亡风险,并改善其生存预后。乳腺癌患者应采取积极治疗手段控制雌二醇水平升高、抑制肿瘤的生长和扩散,从而提高患者的生存率。展开更多
针对混频数据的建模问题,提出自回归U-MIDAS(unrestricted mixed data sampling)分位回归模型.首先,结合嵌套Lasso惩罚方法及spike-and-slab先验进行Bayes参数估计和变量选择;其次,通过数值模拟证明该方法的优越性;最后,将该方法用于美...针对混频数据的建模问题,提出自回归U-MIDAS(unrestricted mixed data sampling)分位回归模型.首先,结合嵌套Lasso惩罚方法及spike-and-slab先验进行Bayes参数估计和变量选择;其次,通过数值模拟证明该方法的优越性;最后,将该方法用于美国名义国内生产总值(GDP)年化季度增长率的预测,结果表明,该方法预测精度较好.展开更多
基金This study was supported by the National Natural Science Foundation of China(42261008,41971034)the Natural Science Foundation of Gansu Province,China(22JR5RA074).
文摘Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation,i.e.,the moisture recycling ratio,but various isotope-based models usually lead to different results,which affects the accuracy of local moisture recycling.In this study,a total of 18 stations from four typical areas in China were selected to compare the performance of isotope-based linear and Bayesian mixing models and to determine local moisture recycling ratio.Among the three vapor sources including advection,transpiration,and surface evaporation,the advection vapor usually played a dominant role,and the contribution of surface evaporation was less than that of transpiration.When the abnormal values were ignored,the arithmetic averages of differences between isotope-based linear and the Bayesian mixing models were 0.9%for transpiration,0.2%for surface evaporation,and–1.1%for advection,respectively,and the medians were 0.5%,0.2%,and–0.8%,respectively.The importance of transpiration was slightly less for most cases when the Bayesian mixing model was applied,and the contribution of advection was relatively larger.The Bayesian mixing model was found to perform better in determining an efficient solution since linear model sometimes resulted in negative contribution ratios.Sensitivity test with two isotope scenarios indicated that the Bayesian model had a relatively low sensitivity to the changes in isotope input,and it was important to accurately estimate the isotopes in precipitation vapor.Generally,the Bayesian mixing model should be recommended instead of a linear model.The findings are useful for understanding the performance of isotope-based linear and Bayesian mixing models under various climate backgrounds.
文摘A Bayesian analysis of the minimal model was proposed where both glucose and insulin were analyzed simultaneously under the insulin-modified intravenous glucose tolerance test (IVGTT). The resulting model was implemented with a nonlinear mixed-effects modeling setup using ordinary differential equations (ODEs), which leads to precise estimation of population parameters by separating the inter- and intra-individual variability. The results indicated that the Bayesian method applied to the glucose-insulin minimal model provided a satisfactory solution with accurate parameter estimates which were numerically stable since the Bayesian method did not require approximation by linearization.
基金supported by the Natural Science Foundation of China(11201345,11271136)
文摘It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure.
文摘[目的]探讨雌二醇(estradiol,E2)水平动态变化与乳腺癌患者生存预后的潜在关联,比较新辅助治疗与无新辅助治疗下乳腺癌患者生存率的差异性。[方法]基于2015—2019年新疆医科大学附属肿瘤医院随访的女性乳腺癌患者的临床数据,首先在不同分位数下(=0.10,0.25,0.50,0.75)分别建立线性分位数混合模型拟合E2水平的动态变化,并通过赤池信息量准则(akaike information criterion,AIC)与贝叶斯信息准则(Bayesian information criteria,BIC)从中选择最优模型作为联合模型的纵向子模型。其次,基于扩展的Cox比例风险模型建立生存子模型;进一步通过共享随机效应建立纵向与生存数据的贝叶斯分位数联合模型,并通过马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法估计其关联系数()。[结果]最优子模型筛选结果显示,=0.50时,纵向子模型的AIC与BIC值最小。在=0.50下构建贝叶斯分位数联合模型。联合模型结果显示,E2水平的动态变化与乳腺癌患者的生存结局显著性相关(=0.59,HR=1.80,95%CI:1.47~2.24)。新辅助治疗是乳腺癌患者的保护因素(HR=0.155,95%CI:0.047~0.384),能够降低乳腺癌患者84.5%死亡风险。[结论]乳腺癌患者E2水平增加与不良生存预后相关,新辅助治疗可降低乳腺癌患者的死亡风险,并改善其生存预后。乳腺癌患者应采取积极治疗手段控制雌二醇水平升高、抑制肿瘤的生长和扩散,从而提高患者的生存率。
文摘针对混频数据的建模问题,提出自回归U-MIDAS(unrestricted mixed data sampling)分位回归模型.首先,结合嵌套Lasso惩罚方法及spike-and-slab先验进行Bayes参数估计和变量选择;其次,通过数值模拟证明该方法的优越性;最后,将该方法用于美国名义国内生产总值(GDP)年化季度增长率的预测,结果表明,该方法预测精度较好.
