Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

We introduce a very general method for high dimensional classification, based on careful
combination of the results of applying an arbitrary base classifier to random projections of …

Recommendation is a prevalent application of machine learning that affects many users;
therefore, it is important for recommender models to be accurate and interpretable. In this …

Quadratic regression (QR) models naturally extend linear models by considering interaction
effects between the covariates. To conduct model selection in QR, it is important to maintain …

Massive online text reviews can be a powerful market research tool for understanding
consumer experiences and helping firms improve and innovate. This research exploits the …

Testing the equality of two means is a fundamental inference problem. For high-dimensional
data, the Hotelling's T 2-test either performs poorly or becomes inapplicable. Several …

Quadratic discriminant analysis (QDA) is a classical and flexible classification approach,
which allows differences between groups not only due to mean vectors but also covariance …

Supplementary material to “Interaction pursuit in high-dimensional multi-response
regression via distance correlation”. Due to space constraints, the details about the post …

Power and reproducibility are key to enabling refined scientific discoveries in contemporary
big data applications with general high-dimensional nonlinear models. In this article, we …

Quadratic discriminant analysis (QDA) is a standard tool for classification due to its simplicity
and exibility. Because the number of its parameters scales quadratically with the number of …