AIC方法在宇宙学模型选择中的可靠性
The reliability of the AIC method in Cosmological Model Selection
摘要：
赤池信息准则（AIC）已被用作一个统计标准，以比较不同的暗能量候选模型的适当性的特定数据集。在适当的条件下，AIC是候选模型A相对于真值T的Kullback-Leibler散度D（T//A）的间接估计。因此，具有较小AIC的暗能量模型被列为更好的模型，因为它与T.在本文中，我们探讨了模型比较过程中估计AIC的统计误差的影响。使用参数自举技术，我们研究了一组候选模型之间的AIC差异的分布，由于在数据中的噪声的不同实现，并表明，这种分布的形状和蔓延可以是相当不同的。我们还研究了在文献中常用的阈值参数的不同值的AIC程序的成功率。对于真实暗能量模型的合理选择，我们的研究表明，除了阈值之外，研究AIC差异的分布对于正确解释使用AIC技术的暗能量模型的比较是有用的。
英文摘要：
The Akaike information criterion (AIC) has been used as a statistical criterion to compare the appropriateness of different dark energy candidate models underlying a particular data set. Under suitable conditions, the AIC is an indirect estimate of the Kullback-Leibler divergence D(T//A) of a candidate model A with respect to the truth T. Thus, a dark energy model with a smaller AIC is ranked as a better model, since it has a smaller Kullback-Leibler discrepancy with T. In this paper, we explore the impact of statistical errors in estimating the AIC during model comparison. Using a parametric bootstrap technique, we study the distribution of AIC differences between a set of candidate models due to different realizations of noise in the data and show that the shape and spread of this distribution can be quite varied. We also study the rate of success of the AIC procedure for different values of a threshold parameter popularly used in the literature. For plausible choices of true dark energy models, our studies suggest that investigating such distributions of AIC differences in addition to the threshold is useful in correctly interpreting comparisons of dark energy models using the AIC technique.
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。
