Wenlu Tang
Biographic Data
| ID | 8921138 |
|---|---|
| NAME | Wenlu Tang |
| GIVEN NAMES | Wenlu |
| FAMILY NAME | Tang |
| SIGNATURE | TANG W |
| AFFILIATIONS | Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China |
| ORCID | 0000-0001-9448-578X |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Quantile Correlation-based Variable Selection
This article is concerned with identifying important features in high-dimensional data analysis, especially when there are complex relationships among predictors. Without any specification of an actual model, we first introduce a multiple testing procedure based on the quantile correlation to select important predictors in high dimensionality. The quantile-correlation statistic is able to capture a wide range of dependence. A stepwise procedure i…
Incorporating Graphical Structure of Predictors in Sparse Quantile Regression
Quantile regression in high-dimensional settings is useful in analyzing high-dimensional heterogeneous data. In this article, different from existing methods in quantile regression which treat all the predictors equally with the same priori, we take advantage of the graphical structure among predictors to improve the performance of parameter estimation, model selection, and prediction in sparse quantile regression. It is shown under mild conditio…
No prominent works on this page.
Incorporating Graphical Structure of Predictors in Sparse Quantile Regression
Quantile regression in high-dimensional settings is useful in analyzing high-dimensional heterogeneous data. In this article, different from existing methods in quantile regression which treat all the predictors equally with the same priori, we take advantage of the graphical structure among predictors to improve the performance of parameter estimation, model selection, and prediction in sparse quantile regression. It is shown under mild conditio…
Quantile Correlation-based Variable Selection
This article is concerned with identifying important features in high-dimensional data analysis, especially when there are complex relationships among predictors. Without any specification of an actual model, we first introduce a multiple testing procedure based on the quantile correlation to select important predictors in high dimensionality. The quantile-correlation statistic is able to capture a wide range of dependence. A stepwise procedure i…
Computer Science (2 works) · Data mining (2 works) · Machine learning (2 works) · Mathematics (2 works) · Quantile (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · A priori and a posteriori (1 work) · Algorithm (1 work) · Artificial Intelligence (1 work)