A simple application of FIC to model selection

2015/06/19 by Paul A. Wiggins, Wiggins, Paul A.
Computer Science · Engineering · #Control Systems and Identification #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.1506.06129

openalex publication_date 2015/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We have recently proposed a new information-based approach to model selection, the Frequentist Information Criterion (FIC), that reconciles information-based and frequentist inference. The purpose of this current paper is to provide a simple example of the application of this criterion and a demonstration of the natural emergence of model complexities with both AIC-like (N⁰) and BIC-like (log N) scaling with observation number N. The application developed is deliberately simplified to make the analysis analytically tractable.

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