AL-Qadisiyah Journal For Administrative and Economic sciences

Bayesian lasso in factorial experiment Designs with application

Document Type : Research Paper

Authors

Maha H. Abed
Mohammed H. AL-Sharoot

University of AL-Qadisiya

https://doi.org/10.33916/qjae.2026.01241250
Abstract
This paper develops the Factorial Bayesian LASSO (FBLASSO), a hierarchical shrinkage method designed for high-dimensional factorial experiment designs where numerous main and interaction effects must be estimated simultaneously. The model incorporates distinct shrinkage parameters for main and interaction terms, enabling adaptive penalization and improving estimation stability in the presence of multicollinearity. A controlled Monte Carlo simulation, reflecting realistic agricultural conditions, demonstrates that FBLASSO achieves lower mean squared error and higher true positive rates compared with Ordinary Least Squares, classical LASSO, and Ridge regression. The method is further applied to a real wheat field experiment conducted in Al-Qadisiyah Governcy, Iraq, involving fertilizer levels, irrigation regimes, and cultivar types. Results show that fertilizer and irrigation exert the strongest main effects on yield, while only a limited subset of interactions is retained by the model. These findings highlight the effectiveness of FBLASSO in recovering influential factorial effects and producing interpretable results in complex agricultural experiments.

Keywords

Bayesian LASSO
Factorial designs
Shrinkage methods
Agricultural experiments
Hierarchical modeling
Crossmark
AL-Qadisiyah Journal For Administrative and Economic sciences
Volume 28, Issue 1
Spring 2026
Pages 241-250

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