Consistent Model Selection by an AutomaticGetsApproach
证明了PcGets选择程序的一致性，并与线性回归中的其他模型选择准则比较，发现PcGets的显著性水平在大样本下与Hannan-Quinn和Schwarz信息准则一致，但有限样本行为不同，且预筛选可提升SIC表现。
Abstract We establish the consistency of the selection procedures embodied in PcGets , and compare their performance with other model selection criteria in linear regressions. The significance levels embedded in the PcGets Liberal and Conservative algorithms coincide in very large samples with those implicit in the Hannan–Quinn (HQ) and Schwarz information criteria (SIC), respectively. Thus, both PcGets rules are consistent under the same conditions as HQ and SIC. However, PcGets has a rather different finite‐sample behaviour. Pre‐selecting to remove many of the candidate variables is confirmed as enhancing the performance of SIC.