估计实对数标准偏差
Estimating Real Log Canonical Thresholds
摘要：
在模型选择问题中，边际似然的估计起着重要的作用.广泛应用的贝叶斯信息准则（WBIC）和奇异贝叶斯信息准则（sBIC）给出了对数边际似然的近似，可以应用于正则和奇异模型。当已知实对数典型阈值时，sBIC的性能被认为优于WBIC，但只有很少的实对数典型阈值是已知的。在本文中，我们提出了一个新的估计的实对数正则阈值的基础上的热力学积分与逆温度的方差。此外，我们提出了一个应用程序，使sBIC广泛适用。最后，我们通过模拟研究和实际数据的应用研究了估计器和模型选择的性能。
英文摘要：
Evaluation of the marginal likelihood plays an important role in model selection problems. The widely applicable Bayesian information criterion (WBIC) and singular Bayesian information criterion (sBIC) give approximations to the log marginal likelihood, which can be applied to both regular and singular models. When the real log canonical thresholds are known, the performance of sBIC is considered to be better than that of WBIC, but only few real log canonical thresholds are known. In this paper, we propose a new estimator of the real log canonical thresholds based on the variance of thermodynamic integration with an inverse temperature. In addition, we propose an application to make sBIC widely applicable. Finally, we investigate the performance of the estimator and model selection by simulation studies and application to real data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
