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

Return predictability has been one of the central research questions in finance for many
decades. This paper proposes a predictive regression with multiple structural changes to …

Abstract Model average techniques are very useful for model-based prediction. However,
most earlier works in this field focused on parametric models and continuous responses. In …

This paper studies data with mass features, commonly observed in applications such as text
classification and medical diagnosis. We allow data to have several structures without …

Many researchers have studied the combinations of machine learning techniques and
traditional statistical strategies, and proposed effective procedures for complicated data sets …

Despite the urgent need for an effective prediction model tailored to individual interests,
existing models have mainly been developed for the mean outcome, targeting average …

Thresholding variable plays a crucial role in subgroup identification for personalized
medicine. Most existing partitioning methods split the sample based on one predictor …

Forward regression, a classical variable screening method, has been widely used for model
building when the number of covariates is relatively low. However, forward regression is …

In this paper, we consider semiparametric varying coefficient partially linear models when
the predictor variables of the linear part are ultra-high dimensional where the dimensionality …

Background Many mathematical and statistical models and algorithms have been proposed
to do biomarker identification in recent years. However, the biomarkers inferred from …