Network datasets typically exhibit certain types of statistical patterns, such as within-dyad correlation, degree heterogeneity, and triadic patterns transitivity clustering. The first two these can be well represented with a social relations model, type additive effects model originally developed for continuous dyadic data. Higher-order multiplicative models, which are related to matrix decomp...

Advances in technology have increased dramatically the amount of data measured in industrial processes. Thousands of measurements are available nowadays in operations where previously only a single measurement, at a given point in time or space, was taken. These measurements allow the reconstruction of the whole profile or "signature" of the operation over time or space. Examples are the tonnag...

We consider additive models built with trend filtering, i.e., additive models whose components are each regularized by the (discrete) total variation of their (k+1)st (discrete) derivative, for a chosen integer k ≥ 0. This results in kth degree piecewise polynomial components, (e.g., k = 0 gives piecewise constant components, k = 1 gives piecewise linear, k = 2 gives piecewise quadratic, etc.)....

The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing them to have either a linear or nonlinear effect on the response. However, the assignment of features to the linear and nonlinear groups is typically assumed known. Thus, to make a GPLAM a viable approach in situatio...