Much work has been done recently to make neural networks more interpretable, and one
approach is to arrange for the network to use only a subset of the available features. In linear …

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 …

Much work has been done recently to make neural networks more interpretable, and one
approach is to arrange for the network to use only a subset of the available features. In linear …

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 …

Various applications of forecasting effective global horizontal irradiance play increasingly
vital role in grid-connected photovoltaic installations, but suffer from forecasting inaccuracy …

We propose a generalization of the lasso that allows the model coefficients to vary as a
function of a general set of some prespecified modifying variables. These modifiers might be …

Forecasting of effective solar irradiation has developed a huge interest in recent decades,
mainly due to its various applications in grid connect photovoltaic installations. This paper …

Including pairwise interactions between the predictors of a regression model can produce
better predicting models. However, to fit such interaction models on typical data sets in …

Though Gaussian graphical models have been widely used in many scientific fields,
relatively limited progress has been made to link graph structures to external covariates. We …

For the etiology, progression, and treatment of complex diseases, gene-environment (GE)
interactions have important implications beyond the main G and E effects. GE interaction …