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Iterative Partial Least Squares with Right-Censored Data Analysis: A Comparison to Other Dimension Reduction Techniques
| Content Provider | Scilit |
|---|---|
| Author | Huang, Jie Harrington, David |
| Copyright Year | 2005 |
| Description | Journal: Biometrics In the linear model with right-censored responses and many potential explanatory variables, regression parameter estimates may be unstable or, when the covariates outnumber the uncensored observations, not estimable. We propose an iterative algorithm for partial least squares, based on the Buckley-James estimating equation, to estimate the covariate effect and predict the response for a future subject with a given set of covariates. We use a leave-two-out cross-validation method for empirically selecting the number of components in the partial least-squares fit that approximately minimizes the error in estimating the covariate effect of a future observation. Simulation studies compare the methods discussed here with other dimension reduction techniques. Data from the AIDS Clinical Trials Group protocol 333 are used to motivate the methodology. |
| Related Links | http://onlinelibrary.wiley.com/doi/10.1111/j.0006-341X.2005.040304.x/pdf |
| Ending Page | 24 |
| Page Count | 8 |
| Starting Page | 17 |
| e-ISSN | 15410420 |
| DOI | 10.1111/j.0006-341x.2005.040304.x |
| Journal | Biometrics |
| Issue Number | 1 |
| Volume Number | 61 |
| Language | English |
| Publisher | Wiley-Blackwell |
| Publisher Date | 2005-03-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Biometrics Mathematical Social Sciences Statistics and Probability Accelerated Failure Time Model Buckley–james Estimation Partial Least Squares |
| Content Type | Text |
| Resource Type | Article |
