Model Selection Techniques: An Overview

In the era of big data, analysts usually explore various statistical models or machine-learning methods for observed data 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 it is central to scientific studies in such fields 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 has been proposed, following different philosophies and exhibiting varying performances. The purpose of this article is to provide 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.

 

 

Vast developments in hardware storage, precision instrument manufacturing, economic globalization, and so forth have generated huge volumes of data that can be analyzed to extract useful information. Typical statistical inference or machinelearning procedures learn from and make predictions on data by fitting parametric or nonparametric models (in a broad Digital Object Identifier 10.1109/MSP.2018.2867638 Date of publication: 13 November 2018 ©istockphoto.com/gremlin Jie Ding, Vahid Tarokh, and Yuhong Yang Model Selection Techniques An overview IEEE Signal Processing Magazine | November 2018 | 17 sense). However, there exists no model that is universally suitable for any data and goal. An improper choice of model or method can lead to purely noisy discoveries, severely misleading conclusions, or disappointing predictive performances.