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Kernel based methods for accelerated failure time model with ultra-high dimensional data
BACKGROUND: Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L(1 )and L(p )penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size n ≪ m (the number of genes), directly...
| Autores principales: | , , , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2010
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019227/ https://www.ncbi.nlm.nih.gov/pubmed/21176134 http://dx.doi.org/10.1186/1471-2105-11-606 |