Variable Selection in Joint Modelling of Longitudinal and Survival Data and Multi-Response Optimization in Designed Experiments

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Advances in information technology have enabled researchers to deal with large data sets with high dimensions routinely and variable selection is hence an essential step in modeling these data sets. Classical variable selection approaches are often implemented after evaluating all possible sub-models. They are computationally intensive and become infeasible with high dimensional data. A class of methods via regularization has been developed to target such applications, and they have been lauded for their computational efficiency and stability. We propose two different interesting research components, which are similar in terms of statistical methodology.