学术报告
Performance Assessment of High-dimensional Variable Identification - Yuhong Yang (University of Minnesota)
题目：Performance Assessment of High-dimensional Variable Identification
报告人： Yuhong Yang (University of Minnesota)
Abstract :One important task in statistical/machine learning is to identify the most important variables that influence the response of interest. Examples include the identification of biomarkers of a disease in medical research and choice of behavioral characteristic variables of customers for effective advertisement. Since such variable selection process is ubiquitous in data analysis, reproducibility of the statistical results demands a serious evaluation of reliability of the employed model selection method, no matter what label it may have in terms of good properties. Instability measures have been proposed for evaluating model selection uncertainty. However, low instability does not necessarily indicate that the selected model is trustworthy, since low instability can also arise when a certain method tends to select an overly parsimonious model. $F-$ and $G$-measures have become increasingly popular for assessing variable selection performance in theoretical studies and simulation results in machine learning and statistics. However, they are not computable in practice. In this work, we propose an estimation method for $F-$ and $G$-measures and prove their desirable properties of uniform consistency. This gives the data analyst a valuable tool to compare different variable selection methods based on the data at hand. Extensive simulations are conducted to show the very good finite sample performance of our approach. We further demonstrate the application of our methods using several micro-array gene expression data sets.
时间： 7月10日（周一）上午10:30-11:30
地点：首都师范大学本部教二楼613教室
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