Model Selection Techniques -- An Overview

TL;DR

This paper provides a comprehensive overview of model selection techniques across various fields, discussing their motivations, theoretical properties, and practical applications to guide reliable data analysis.

Contribution

It offers an integrated review of diverse model selection methods, highlighting their theoretical foundations, performance, and applicability in scientific research.

Findings

Summarizes key model selection criteria and their theoretical properties

Discusses performance and applicability of methods in large samples

Shares insights on controversial aspects of model selection practice

Abstract

In the era of big data, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction, and thus central to scientific studies in fields such as ecology, economics, engineering, finance, political science, biology, and epidemiology. There has been a long history of model selection techniques that arise from researches in statistics, information theory, and signal processing. A considerable number of methods have been proposed, following different philosophies and exhibiting varying performances. The purpose of this article is to bring a comprehensive overview of them, in terms of their motivation, large sample performance, and applicability. We provide integrated and practically relevant discussions on theoretical properties of state-of- the-art model selection approaches. We also share our thoughts on some controversial views on the practice of model selection.