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

信息技术的进步使研究人员能够例行公事地处理高维的大数据集，因此变量选择是对这些数据集进行建模的关键步骤。经典的变量选择方法通常是在评估所有可能的子模型之后实施的。它们是计算密集型的，对于高维数据变得不可行。一类通过正则化的方法已经被开发出来针对这类应用，它们的计算效率和稳定性得到了赞扬。我们提出了两个不同的有趣的研究组成部分，它们在统计方法上是相似的。

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.