Abstract
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 Buckland et al. (1997) and Burnham and Anderson (2003), which are termed as smoothed AIC (SAIC) and smoothed BIC (SBIC) methods. Because of simplicity and applicability, these methods are very widely used in many elds. By using an illustrative example and deriving limiting properties for the weights in linear regression, we find that the existing variance estimation for SAIC is incorrect, but for SBIC, it is. We propose a simulation-based inference for SAIC. The simulation results show its promising performance.
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