Theoretical Considerations Surrounding Point Process Adaptive Filtering

Inference in Perturbation Models, Mixtures and Spatial Scan Processes

Bayesian Clustering for Identifying Alternative Splice Variants

Mean Squared Prediction Error Estimation in Mixed Models

Kernel Regularization and Dimension Reduction

Hierarchical Modal Clustering based on the Topography of High-dimensional Mixtures

Implications of Neuronal Heterogeneity on Population Coding

Statistical Learning with Constraints

Concave Learners for RankBoost

On local minimax estimation with some consequences for estimator fusion and deterministic or random forests