Keywords
Lasso, Ridge, Elastic net, Maize Data
Abstract
In high-dimensional genomic data analysis, traditional linear regression techniques often struggle due to the presence of a large number of predictor variables relative to observations. Penalized regression methods such as LASSO, Ridge, and Elastic Net have emerged as effective solutions by imposing regularization, which helps in managing multicollinearity and enhancing prediction accuracy. This study applies these techniques to the Maize dataset to model the time to male flowering, selecting relevant genetic markers as predictors. Our findings suggest that Elastic Net is particularly effective for high-dimensional data with correlated variables, achieving a balance between prediction accuracy and variable selection. The results are consistent with previous findings in genomic studies, including the work of Waldmann et al.
Semester
Fall 2025
Course Name
STA 6366 Data Science 1
Instructor Name
Dr. Rui Xie
College
College of Sciences
STARS Citation
Ahiduzzaman, Md, "Comparative Analysis of LASSO, Ridge, and Elastic Net for Variable Selection in High-Dimensional Maize Data" (2025). Data Science and Data Mining. 49.
https://stars.library.ucf.edu/data-science-mining/49
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Included in
Applied Statistics Commons, Data Science Commons, Statistical Methodology Commons, Statistical Models Commons