Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data

Spearman Rank Correlation Coefficient

A selective overview of feature screening for ultrahigh-dimensional data.

High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of high-dimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that ...

Feature Screening for Ultrahigh Dimensional Categorical Data with Applications.

Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for categorical response with ultrahigh dimensional categorical covariates. The proposed procedure can be directly ...

Conditional quantile screening in ultrahigh - dimensional heterogeneous data

To accommodate the heterogeneity that is often present in ultrahigh-dimensional data, we propose a conditional quantile screening method, which enables us to select features that contribute to the conditional quantile of the response given the covariates. The method can naturally handle censored data by incorporating a weighting scheme through redistribution of the mass to the right; moreover, ...

Smoothed Rank Regression With Censored Data

A weighted rank estimating function is proposed to estimate the regression parameter vector in an accelerated failure time model with right censored data. In general, rank estimating functions are discontinuous in the regression parameter, creating difficulties in determining the asymptotic distribution of the estimator. A local distribution function is used to create a rank based estimating fu...