Factor selection in screening experiments by aggregation over random models

Rakhi Singh and John Stufken

Computational Statistics & Data Analysis, 2024, vol. 194, issue C

Abstract: Screening experiments are useful for identifying a small number of truly important factors from a large number of potentially important factors. The Gauss-Dantzig Selector (GDS) is often the preferred analysis method for screening experiments. Just considering main-effects models can result in erroneous conclusions, but including interaction terms, even if restricted to two-factor interactions, increases the number of model terms dramatically and challenges the GDS analysis. A new analysis method, called Gauss-Dantzig Selector Aggregation over Random Models (GDS-ARM), which performs a GDS analysis on multiple models that include only some randomly selected interactions, is proposed. Results from these different analyses are then aggregated to identify the important factors. The proposed method is discussed, the appropriate choices for the tuning parameters are suggested, and the performance of the method is studied on real and simulated data.

Keywords: Dantzig selector; Main-effects; Screening performance; Supersaturated designs; Two-factor interactions (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947324000240
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:194:y:2024:i:c:s0167947324000240

DOI: 10.1016/j.csda.2024.107940

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().