Construction of Supersaturated Designs with Small Coherence for Variable Selection

Qi, Youran; Chien, Peter

doi:10.51387/23-NEJSDS34

The New England Journal of Statistics in Data Science

Construction of Supersaturated Designs with Small Coherence for Variable Selection

Volume 1, Issue 3 (2023), pp. 323–333

Youran Qi Peter Chien

https://doi.org/10.51387/23-NEJSDS34

Pub. online: 5 June 2023 Type: Methodology Article

Open Access

Area: Machine Learning and Data Mining

Accepted
19 April 2023

Published
5 June 2023

Abstract

The supersaturated design is often used to discover important factors in an experiment with a large number of factors and a small number of runs. We propose a method for constructing supersaturated designs with small coherence. Such designs are useful for variable selection methods such as the Lasso. Examples are provided to illustrate the proposed method.

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Keywords

Design of Experiments Supersaturated Design Unbalanced Design Coherence Linear Model Variable Selection

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