Technical Reports, Department of Statistics, Northwestern University

• 99-01 Jiang, W. (1999). Large Time Behavior of Boosting Algorithms for Regression and Classification
• 00-01 Jiang, W. (2000). On Weak Base Hypotheses and Their Implications for Boosting Regression and Classification. (Revision of 99-01 .)
• 00-02 Jiang, W. (2000). On Weak Base Learners for Boosting Regression and Classification.
• 00-03 Jiang, W. (2000). Does Boosting Overfit: Views from an Exact Solution.
• 00-04 Jiang, W. (2000). Is Regularization Unnecessary for Boosting?
• 00-05 Jiang, W. (2000). Process Consistency for AdaBoost.
• 01-01 Jiang, W. (2001). Some Theoretical Aspects of Boosting in the Presence of Noisy Data.
• 05-01 Jiang, W. (2005). On Consistency of Bayesian Variable Selection for High Dimensional Binary Regression and Classification.
• 05-02 Jiang, W. (2005). Bayesian Variable Selection for High Dimensional Generalized Linear Models.
• 07-01 Jiang, W. and Tanner, M. A. (2007). Gibbs posterior for variable selection in high dimensional classification and data mining.
• 07-02 Jiang, W. and Tanner, M. A. (2008). Risk minimization for time series binary choice with variable selection.