Multimodel inference based on smoothed information criteria

• 作者: Shangwei Zhao Xinyu Zhang
• 来源期刊: 中国科学：数学（英文版）
• 年，卷（期）: 2021,（11）
• 所属期刊栏目: Articles
• 页码范围: 2563-2578
• 页数: 16页
• 语种: 英文

摘要

The multimodel inference makes statistical inferences from a set of plausible models rather than from a single model.In this paper,we focus on the multimodel inference based on smoothed information criteria proposed by seminal monographs (see Buckland et al.(1997) and Burnham and Anderson (2003)),which are termed as smoothed Akaike information criterion (SAIC) and smoothed Bayesian information criterion (SBIC)methods.Due to their simplicity and applicability,these methods are very widely used in many fields.By using an illustrative example and deriving limiting properties for the weights in the linear regression,we find that the existing variance estimation for SAIC is not applicable because of a restrictive condition,but for SBIC it is applicable.Especially,we propose a simulation-based inference for SAIC based on the limiting properties.Both the simulation study and the real data example show the promising performance of the proposed simulation-based inference.