|Reference: ||1. Alizadeh A. A., Eisen M. B., Eric Davis R., Ma C., Lossos I. S., Rosenwald A., Boldrick J. C., Sabet H., Tran T., Yu X., Powell J. I., Yang L., Marti G. E., Moore T., Hudson J. Jr, Lu L., Lewis D. B., Tibshirani R., Sherlock G., Chan W. C., Greiner T. C., Weisenburger D. D., Armitage J. O., Warnke R., Levy R., Wilson W., Grever M. R., Byrd J. C., Botstein D., Brown P. O., and Staudt L. M. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403, 503-511.|
2. Beadle, G., Come, S., Henderson, C., Silver, B., and Hellman, S. (1984). The effect of adjuvent chemotherapy on the cosmetic results after primary radiation treatment for early stage breast cancer. International Journal of Radiation Oncology, Biology and Physics, 10, 2131-2137.
3. Bertsekas, D. P. (1982). Projected Newton methods for optimization problems with simple constraints. SIAM Control and Optimization, 20, 221-246.
4. Cox, D. R. (1972). Regression models and life-tables. Journal of Royal Statistical Society, Series B, 34, 187-220
5. Craig, B. A., Black, M. A. and Doerge, R. W. (2003). Gene expression data: The technology and statistical analysis. Journal of Agricultural, Biological, and Environmental Statistic, 8, 1-28.
6. Dykstra, R. L. and Kuo, H. C. (2003). Constrained non-parametric estimation under arbitrarily grouped, censored, and truncated data. A thesis submitted in partial fulfillment of the requirement for the Doctor of Philosophy degree in Statistics in the Graduate College of The University of Iowa.
7. Friedman, J. H. and Popescu, B. E. (2004). Gradient directed regularization for linear regression and classification. Technical report, Department of Statistics, Stanford University. http://www-stat.stanford.edu/~jhf/PathSeeker.html
8. Gui, J. and Li, H. (2005). Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics, In press.
9. Huang, Y. W. (2004). The comparison of parameter estimation with application to Massachusetts heath care panel study. A thesis submitted in partial fulfillment of the requirement for the Master Science degree in Mathematic in National Sun Yat-Sen University.
10. Jolliffe I.T. (1986). Principal component analysis. New York: Springer-Verlag.
11. Ma, S. and Huang, J. (2005). Clustered threshold gradient directed regularization: with applications to survival analysis using microarray data. Technical Report No. 348, Department of Statistics and Actuarial Science, University of Iowa.
12. Pan W. (1997). Extending the iterative convex minorant algorithm to the Cox model. Report 1997-013, Division of Biostatistics, University of Minnesota.
13. Park P. J., Tian L. and Kohane I. S. (2002). Linking gene expression data with patient survival times using partial least squares. Bioinformatics, 18, S120-S127.
14. Petroni, G. R. and Wolfe, R. A. (1994). A two-sample test for stochastic ordering with interval-censored data. Biometrics, 50, 77-87.
15. Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, B, 58, 267-288.
16. Turnbull, B. W. (1976). The empirical distribution function with arbitrarily grouped, censored, and truncated data. Journal of the Royal Statistical Society, B, 38, 290-295.
17. Wold, H. (1966). Estimation of principal components and related models by iterative least squares. In Multivariate Analysis, Ed. P.R. Krishnaiah, New York: Academic Press, 391-420.