Name
Abstract | ||
|---|---|---|
AbstractTo account for censoring in estimating the accelerated failure time AFT model with right censored data, the weighted least squares regression WLSR has been developed by using the inverse-censoring-probability weights. However, it is well known that the traditional ordinary least squares may fail to produce a reliable estimator for data subject to heavy-tailed errors or outliers. For robust estimation in the AFT model, we propose the weighted composite quantile regression WCQR method, in which the sum of weighted multiple quantile objective functions based on the inverse-censoring-probability weights is used as a loss function. As a novel regularisation method for right censored data, we further propose the adaptive lasso penalised WCQR AWCQR method in order to perform simultaneous estimation and variable selection. The large sample properties of the WCQR and AWCQR estimators are established under some regularity conditions. The proposed methods are evaluated through simulation studies and real data applications. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1504/IJDMB.2016.076015 | Periodicals |
Keywords | Field | DocType |
adaptive lasso, censoring, composite quantile regression, inverse censoring probability, variable selection | Least squares,Ordinary least squares,Lasso (statistics),Outlier,Quantile,Statistics,Censoring (statistics),Mathematics,Quantile regression,Estimator | Journal |
Volume | Issue | ISSN |
15 | 1 | 1748-5673 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sungwan Bang | 1 | 14 | 2.89 |
| Hyungjun Cho | 2 | 104 | 8.44 |
| Myoungshic Jhun | 3 | 27 | 6.75 |