Big Data bring new opportunities to modern society and challenges to data scientists. On the
one hand, Big Data hold great promises for discovering subtle population patterns and …

The estimation of large covariance and precision matrices is fundamental in modern
multivariate analysis. However, problems arise from the statistical analysis of large panel …

We survey the nascent literature on machine learning in the study of financial markets. We
highlight the best examples of what this line of research has to offer and recommend …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Abstract The World Energy Council releases the Energy Trilemma Index (ETI) report
annually primarily to assess the energy performance of countries worldwide. Nevertheless …

Abstract The Davis–Kahan theorem is used in the analysis of many statistical procedures to
bound the distance between subspaces spanned by population eigenvectors and their …

Standard estimators of risk premia in linear asset pricing models are biased if some priced
factors are omitted. We propose a three-pass method to estimate the risk premium of an …

This paper considers generalized least squares (GLS) estimation for linear panel data
models. By estimating the large error covariance matrix consistently, the proposed feasible …

It is known that the common factors in a large panel of data can be consistently estimated by
the method of principal components, and principal components can be constructed by …

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