Peter Hall's work illuminated many aspects of statistical thought, some of which are very well
known including the bootstrap and smoothing. However, he also explored many other lesser …

Fitting insurance loss data can be challenging because of their non-negativity, asymmetry,
skewness, and possible multi-modality. Though many parametric models have been used in …

Despite the popularity of high dimension, low sample size data analysis, there has not been
enough attention to the sample integrity issue, in particular, a possibility of outliers in the …

Discrimination with high dimensional data is often more effectively done with sparse
methods that use a fraction of predictors rather than using all the available ones. In recent …

The support vector machine (SVM) is a powerful and widely used classification algorithm.
This paper uses the Karush-Kuhn-Tucker conditions to provide rigorous mathematical proof …

The classification of high-dimensional data is a very important problem that has been
studied for a long time. Many studies have proposed linear classifiers based on Fisher's …

In this work, we characterize two data piling phenomenon for a high-dimensional binary
classification problem with heterogeneous covariance models. The data piling refers to the …

Classification in high dimensions has been highlighted for the past two decades since
Fisher's linear discriminant analysis (LDA) is not optimal in a smaller sample size n …

Sequencing-based technologies provide an abundance of high-dimensional biological
datasets with skewed and zero-inflated measurements. Classification of such data with …

Data piling refers to the phenomenon that training data vectors from each class project to a
single point for classification. While this interesting phenomenon has been a key to …