Manuscripts and Technical Reports

• Chen, B., Chen, L., Lin, Y. and Ramakrishnan, R. (2005). Prediction Cubes . Accepted, Proceedings of the 31st VLDB (very large database) Conference. Trondheim, Norway.

• Yuan, M. and Lin, Y. (2005). Model Selection and Estimation in the Gaussian Graphical Model .

• Lin, Y. and Yuan, M. (2005). Convergence Rates of Compactly Supported Radial Basis Function Regularization . Accepted, Statistica Sinica.

• Jeon, Y. and Lin, Y. (2004). An Effective Method for High Dimensional Log-density ANOVA Estimation, with Application to Nonparametric Graphical Model Building . TR 1100. Accepted, Statistica Sinica.

• Lin, Y. and Jeon, Y. (2004). A Characterization of the Log-density Smoothing Spline ANOVA Model . TR 1098.

• Yuan, M. and Lin, Y. (2004). Model Selection and Estimation in Regression with Grouped Variables . TR 1095. Accepted, JRSSB.

• Zhang, H. H. and Lin, Y. (2004). Component Selection and Smoothing for Nonparametric Regression in Exponential Families . Accepted, Statistica Sinica.

• Yuan, M. and Lin, Y. (2004). Efficient Empirical Bayes Model Selection and Estimation in Linear Models . TR 1092r. Accepted, Journal of American Statistical Association.

• Leng, C., Lin, Y. and Wahba, G. (2004). A Note on the Lasso and Related Procedures in Model Selection . TR 1091r. Accepted, Statistica Sinica.

• Lin, Y. and Zhang, H. H. (2003). Component Selection and Smoothing in Smoothing Spline Analysis of Variance Models . TR 1072. Revised version accepted by Annals of Statistics. Matlab codes for the cosso. After downloading, use the command "tar -xvf filename" to extract the files.

• Brown, L. D. and Lin, Y. (2003). Racetrack Betting and Consensus of Subjective Probabilities . Statistics and Probability Letters. 62, 175-187.

• Lee, Y., Lin, Y. and Wahba, G. (2004). Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data . Journal of American Statistical Association. 99. 67-81.

• Lin, Y. and Brown, L. D. (2004). Statistical Properties of the Method of Regularization with Periodic Gaussian Reproducing Kernel . Annals of Statistics. 32. 1723-1743.

• Zhang, H., Wahba, G., Lin, Y., Voelker, M., Ferris, M., Klein, R. and Klein, B. (2004). Variable Selection and Model Building via Likelihood Basis Pursuit . Journal of American Statistical Association. 99. 659-672.

• Lin, Y. and Jeon, Y. (2002). Random Forests and Adaptive Nearest Neighbors . Accepted, Journal of American Statistical Association.

• Wahba, G., Lin, Y., and Leng, C. (2002) Penalized Log Likelihood Density Estimation via Smoothing-Spline ANOVA and ranGACV - Comments to Hansen and Kooperberg `Spline Adaptation in Extended Linear Models' . Statistical Science 17. 33-37.

• Lin, Y. (2001) A Note on Margin-based Loss Functions in Classification . TR 1044r. Revised and simplified version ( here ) was published in Statistics and Probability Letters 68/1. 73-82.

• Brown, L. D. and Lin, Y. (2002) Discussion of ``Random Rates in Anisotropic Regression'' by Hoffmann and Lepski . Annals of Statistics 30. 363-370.

• Wahba, G., Lin, Y., Lee, Y. and Zhang, H. (2001) Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines . In Nonlinear Estimation and Classification, Denison, Hansen, Holmes, Mallick and Yu, eds, Springer, 125-143, 2002.

• Lin, Y. and Jeon, Y. (2003) Discriminant Analysis through a Semi-parametric Model . Source code for TDA and Example Usage. Biometrika 90. 379-392.

• Lin, Y., Nadler, S. T., Lan, H., Attie, A. D., and Yandell, B. S. (2002) Adaptive gene picking with microarray data: detecting important low abundance signals. . In The Analysis of Gene Expression Data: Methods and Software, ed by G Parmigiani, ES Garrett, RA Irizarry, SL Zeger. Springer-Verlag (to appear), ch. 13.

• Lin, Y. (2000) Some Asymptotic Properties of the Support Vector Machine . TR 1029r. Revised February 2002.

• Lin, Y., Wahba, G., Zhang, H., and Lee, Y. (2002) Statistical Properties and Adaptive Tuning of Support Vector Machines . Machine Learning . 48, 115-136.

• Lin, Y., Lee, Y., and Wahba, G. (2002) Support Vector Machines for Classification in Nonstandard Situations . Machine Learning . 46, 191-202.

• Lin, Y. (2002) Support Vector Machines and the Bayes Rule in Classification . Data Mining and Knowledge Discovery . 6. 259-275.

• Wahba, G., Lin, Y., and Zhang, H. (2000) Generalized Approximate Cross Validation for Support Vector Machines, or, Another Way to Look at Margin-Like Quantities . Advances in Large Margin Classifiers , 297-309, Smola, Bartlett, Scholkopf and Schurmans, eds., MIT Press.

• Lin, Y. (2000) Tensor Product Space ANOVA Models . Annals of Statistics . 28, 734-755.

Acknowledgement: The research reported here is partially supported by the National Science Foundation under Grant No. 0134987.