Sure Explained Variability and Independence Screening
发布时间：2017-12-14 点击次数：
| 报告题目： | Sure Explained Variability and Independence Screening |
| 报 告 人： | Prof. Min Chen(Academy of Mathematics and Systems Science,CAS) |
| 报告时间： | 2017年12月15日 15:30--16:30 |
| 报告地点： | 数学院三楼报告厅 |
| 报告摘要： |
In the era of Big Data, extracting the most important exploratory variables available in ultrahigh dimensional data plays a key role in scienti_c researches. Existing researches have been mainly focusing on applying the extracted exploratory variables to describe the central tendency of their related response variables. For a response variable, its variability characteristic is as much important as the central tendency in statistical inference. This paper focuses on the variability and proposes a new model-free feature screening approach: sure explained variability and inde-
pendence screening (SEVIS). The core of SEVIS is to take the advantage of recently proposed asymmetric and nonlinear generalized measures of correlation in the screening. Under some mild conditions, the paper shows that SEVIS not only possesses desired sure screening property and ranking consistency property, but also is a computational convenient variable selection method to deal with ultrahigh-dimensional data sets with more features than observations. The superior performance of SEVIS, compared with existing model-free methods, is illustrated in extensive simulations. A real example in ultrahigh-dimensional variable selection demonstrates that the variables selected by SEVIS better explain not only the response variables, but also the variables selected by other methods. |