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 …

In this paper, a new prediction model for aggregated loads of buildings has been propose.
Due to high correlation of prediction performance with related horizons and aggregated …

This article considers high-dimensional image-on-scalar regression, where the spatial
heterogeneity of covariate effects on imaging responses is investigated via a flexible …

In this article, we propose to measure the dependence between two random variables
through a composite coefficient of determination (CCD) of a set of nonparametric …

In this paper, we consider a new mixture of varying coefficient models, in which each mixture
component follows a varying coefficient model and the mixing proportions and dispersion …

It is routine practice in statistical modelling to first select variables and then make inference
for the selected model as in stepwise regression. Such inference is made upon the …

In this paper, we propose a new nonparametric estimator of time-varying forecast
combination weights. When the number of individual forecasts is small, we study the …

We explore time-varying networks for high-dimensional locally stationary time series, using
the large VAR model framework with both the transition and (error) precision matrices …

In this paper, a two-stage estimation procedure is proposed for varying coefficient partially
nonlinear models, in which the estimates of parametric vector and coefficient functions do …