Generalized linear mixed models are a widely used tool for modeling longitudinal data. However, their use is typically restricted to few covariates, because the presence of many predictors yields unstable estimates. The presented approach to the fitting of generalized linear mixed models includes an L 1 -penalty term that enforces variable selection and shrinkage simultaneously. A gradient ascent algorithm is proposed that allows to maximize the penalized loglikelihood yielding models with reduced complexity. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available. The method is investigated in simulation studies and illustrated by use of real data sets.

Disease-free infection in HIV-infected adults is associated with HLA-mediated suppression of viremia, whereas in the sooty mangabey and other healthy natural hosts of SIV, viral replication continues unabated. To better understand factors preventing HIV disease, we here investigated pediatric infection, where AIDS typically develops more rapidly than in adults. Among 170 non-progressing anti-retroviral therapy-naïve children aged >5yrs maintaining normal-for-age CD4 T-cell counts, immune activation levels were low despite high viremia (median 26,000 copies/ml). Potent, broadly neutralizing antibody responses in the majority of subjects and strong virus-specific T-cell activity were present but did not drive pediatric non-progression. However, reduced CCR5 expression and low HIV infection in long-lived central memory CD4 T-cells were observed in pediatric non-progressors. These children therefore express two cardinal immunological features of non-pathogenic SIV infection in sooty mangabeys - low immune activation despite high viremia and low CCR5 expression on long-lived central memory CD4 T-cells – suggesting closer similarities with non-pathogenetic mechanisms evolved over thousands of years in natural SIV hosts than those operating in HIV-infected adults.

This tutorial article demonstrates how time-to-event data can be modelled in a very flexible way by taking advantage of advanced inference methods that have recently been developed for generalized additive mixed models. In particular, we describe the necessary pre-processing steps for transforming such data into a suitable format and show how a variety of effects, including a smooth nonlinear baseline hazard, and potentially nonlinear and nonlinearly time-varying effects, can be estimated and interpreted. We also present useful graphical tools for model evaluation and interpretation of the estimated effects. Throughout, we demonstrate this approach using various application examples. The article is accompanied by a new R-package called pammtools implementing all of the tools described here.

The application of machine learning (ML) to the field of orthopaedic surgery is rapidly increasing, but many surgeons remain unfamiliar with the nuances of this novel technique. With this editorial, we address a fundamental topic-the differences between ML techniques and traditional statistics. By doing so, we aim to further familiarize the reader with the new opportunities available thanks to the ML approach.

We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit. Our goal is to shed light on the dependence between the severity distribution of these losses and a set of a set of macroeconomic, financial and firmspecific factors. To do so, we use Generalized Pareto regression techniques, where both the scale and shape parameters are assumed to be functions of these explanatory variables. We perform the selection of the relevant covariates with a state-of-the-art penalized-likelihood estimation procedure relying on L 1 -penalty terms. A simulation study indicates that this approach efficiently selects covariates of interest and tackles spurious regression issues encountered when dealing with integrated time series. Lastly, we illustrate the impact of different economic scenarios on the requested capital for operational risk. Our results have important implications in terms of risk management and regulatory policy.