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

Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series.
However, as the number of component series is increased, the VAR model becomes …

We propose four different estimators that take into account the autocorrelation structure
when reconciling forecasts in a temporal hierarchy. Combining forecasts from multiple …

Covariance matrix estimation plays an important role in statistical analysis in many fields,
including (but not limited to) portfolio allocation and risk management in finance, graphical …

Previously in “Reconciling solar forecasts: Geographical hierarchy”[Solar Energy 146 (2017)
276–286], we have demonstrated that by reconciling the forecasts obtained from a …

Demanding sparsity in estimated models has become a routine practice in statistics. In many
situations, we wish to require that the sparsity patterns attained honor certain problem …

Variable selection for models including interactions between explanatory variables often
needs to obey certain hierarchical constraints. Weak or strong structural hierarchy requires …

Adaptive estimation in structured factor models with applications to overlapping clustering
Page 1 The Annals of Statistics 2020, Vol. 48, No. 4, 2055–2081 https://doi.org/10.1214/19-AOS1877 …

In many applications, data come with a natural ordering. This ordering can often induce local
dependence among nearby variables. However, in complex data, the width of this …

This paper addresses the task of estimating a covariance matrix under a patternless sparsity
assumption. In contrast to existing approaches based on thresholding or shrinkage …