In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef...In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.展开更多
Saturated row-column designs are studied to eliminate positional effects in primary high- throughput screening experiments. Compared to designs currently used in practice, all compounds in the designs considered are c...Saturated row-column designs are studied to eliminate positional effects in primary high- throughput screening experiments. Compared to designs currently used in practice, all compounds in the designs considered are comparable within each microplate in spite of the existence of row and column effects. The designs considered in this paper also have the maximum number of compounds arranged in each microplate. Two statistical methods are used to choose leading compounds in the designs considered. These two methods take full advantages of effect sparsity in primary screening. Simulation studies are carried out to compare the two statistical methods with two ad hoc methods in selecting active compounds. A method that maintains balanced false positives and false negatives is recommended.展开更多
基金Outstanding Youth Foundation of Hunan Provincial Department of Education(Grant No.22B0911)。
文摘In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.
文摘Saturated row-column designs are studied to eliminate positional effects in primary high- throughput screening experiments. Compared to designs currently used in practice, all compounds in the designs considered are comparable within each microplate in spite of the existence of row and column effects. The designs considered in this paper also have the maximum number of compounds arranged in each microplate. Two statistical methods are used to choose leading compounds in the designs considered. These two methods take full advantages of effect sparsity in primary screening. Simulation studies are carried out to compare the two statistical methods with two ad hoc methods in selecting active compounds. A method that maintains balanced false positives and false negatives is recommended.
