Dr. Syed Ejaz Ahmed (Brock University) will present at the February SI Virtual Working Group Meetings (2023-2024) on Wednesday, February 28 at 2:00pm EST (1:00pm CST/12:00pm MST/11:00am PST).

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Presentation Title

Sparse Prediction Strategies in High-dimensional Data Analytics


In high-dimensional data analysis many penalized methods were introduced for simultaneous variable selection and parameters estimation when the model is sparse. However, a model may have sparse signals as well as predictors with weak signals. In this scenario variable selection methods may not distinguish predictors with weak signals and sparse signals. For this reason, we propose post-shrinkage strategies to improve the prediction performance of a selected submodel, and the relative performance of the proposed strategy is appraised by theoretical and simulation studies, respectively. Finally, we consider a brain network connectivity edge weight data to illustrate the performance of the proposed estimators. This data comprises longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) effective brain connectivity network and genetic study data.


S. Ejaz Ahmed, Feryaal Ahmed and B. Yuzbasi (2023). Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data. CRC Press, USA