超高维删失数据的联合特征筛选方法研究

For ultra-high-dimensional censored data, feature screening can be performed to remove noise in big data, and classical statistic analysis can be applied after that. This paper proposes a robust partial correlation coefficient for feature screening, and introduces an inverse probability weighting method to deal with censoring. Based on that, a new joint feature screening method is developed. By incorporating the information of the entire conditional distribution of the failure time, our method can depict the relationship between the response and covariates comprehensively. Compared with the traditional Pearson partial correlation coefficient, this measurement is robust to outliers, heavy-tailed distribution and heteroscedasticity. Moreover, the joint feature screening method proposed based on this metric eliminates the interference caused by the correlation between the covariates through the projection effect, so as to reduce the false negative errors, false positive errors and tackle the problem of collinearity of covariates. We establish the sure screening property of our method and give the details of the iterative algorithm. The competence of our method is further confirmed through comprehensive simulation studies and a real data example.