Regression and time series model selection using variants of the Schwarz information criterion
Regression and time series model selection using variants of the Schwarz information criterion
复制标题
DOI：
10.1080/03610929708831934
复制
发表时间：
1997-01-01
影响因子：
0.8
通讯作者：
Cavanaugh, JE
中科院分区：
文献类型：
--
作者：
Neath, AA;Cavanaugh, JE
施瓦茨（1978）的信息准则SIC是模型选择中广泛使用的工具，主要是由于其计算简单性和在许多建模框架中的有效性能。SIC（施瓦茨，1978）的推导建立了作为候选模型的贝叶斯后验概率变换的渐近近似的标准。在本文中，我们调查的推导过程中被丢弃的渐近忽略不计，但这可能是显着的，在小到中等的样本量的应用程序的识别。我们建议根据这些术语的列入情况制定若干SIC变体。仿真研究的结果表明，瓦片变量提高SIC的性能在两个重要的领域或应用：多元线性回归和时间序列分析。
The Schwarz (1978) information criterion, SIC, is a widely-used tool in model selection, largely due to its computational simplicity and effective performance in many modeling frameworks. The derivation of SIC (Schwarz, 1978) establishes the criterion as an asymptotic approximation to a transformation of the Bayesian posterior probability of a candidate model. In this paper, we investigate the derivation for the identification of terms which are discarded as being asymptotically negligible, but which may be significant in small to moderate sample-size applications. We suggest several SIC variants based on the inclusion of these terms. The results of a simulation study show that tile variants improve upon the performance of SIC in two important areas or application: multiple linear regression and time series analysis.