Smoothing ℓ1\ell_1-penalized estimators for high-dimensional time-course data

When a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We use $\ell_1$-penalized estimators like the Lasso or the Adaptive Lasso which can simultaneously do parameter estimation and model selection. We show that for a time-course of high-dimensional linear models the convergence rates of the Lasso and of the Adaptive Lasso can be improved by combining the different time-points in a suitable way. Moreover, the Adaptive Lasso still enjoys oracle properties and consistent variable selection. The finite sample properties of the proposed methods are illustrated on simulated data and on a real problem of motif finding in DNA sequences.Comment: Published in at http://dx.doi.org/10.1214/07-EJS103 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Discussion: One-step sparse estimates in nonconcave penalized likelihood models

Discussion of ``One-step sparse estimates in nonconcave penalized likelihood models'' [arXiv:0808.1012]Comment: Published in at http://dx.doi.org/10.1214/07-AOS0316A the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Kelly Chibale: Learning to Fail Your Way to Success

A portrait is given of Professor Kelly Chibale, the driving force behind H3D, the drug discovery and development centre at the University of Cape Town, South Africa. He is dedicated to the development of new drugs to fight infectious diseases of poverty that are prevalent in Africa, including MMV390048, the first anti-malarial drug ever to be validated in a Phase I trial in Africa. In 2018 he was recognized as one of Fortune magazine’s top 50 ‘World’s Greatest Leaders’

Jonathan L. Vennerstrom: I Was Standing on the Shoulders of Giants

Jonathan L. Vennerstrom grew up in Ethiopia, where he was witness to the effects of leprosy and other infectious diseases of poverty on the local population. After his studies in organic chemistry, he began to research antiprotozoal agents, which resulted in the discovery of the synthetic ozonides. Fruitful collaborations with the Swiss Tropical and Public Health Institute (Swiss TPH) and the Medicines for Malaria Venture (MMV) have paved the way for antimalarial and antischistosomal drugs. In recognition of his many achievements, Vennerstrom received the 2019 ACS Award for Creative Invention

Anna K. H. Hirsch: Drug Design and Optimisation to Disarm Dangerous Germs

Anna K. H. Hirsch, Professor and Head of the Department of Drug Design and Optimisation at the Helmholtz Institute for Pharmaceutical Research in Saarbrücken (HIPS), Germany, is dedicated to fighting infectious diseases by focussing on enzymes that are central to the metabolism of parasites. Her research has led to a valuable collaboration with the Swiss Tropical and Public Health Institute (Swiss TPH)

Basel: A Hotspot for Drug Discovery and Development Against Poverty-Related Diseases

In discussion with Lukas Meier from the Swiss Tropical and Public Health Institute (Swiss TPH), Lutz Hegemann, Head of Novartis Global Health and Sustainability and Marcel Tanner, President of the Swiss Academies of Arts and Sciences, give their opinions on the changes that occurred in drug discovery and development for poverty-related diseases over the past 30 years. They emphasise the power of public–private partnerships and provide their points of views on what needs to be done in the future to ensure that the poorest of the poor also have access to important therapie

Rain, emotions and voting for the Status Quo

Do emotions affect the decision between change and the status quo? We exploit exogenous variation in emotions caused by rain and analyze data on more than 400 ballot propositions in Switzerland for the years 1958 to 2014 to address this question. The empirical tests are based on administrative ballot outcomes and individual postvote survey data. We find that rain decreases the share of votes for a change. Our robustness checks suggest that changes in the composition of the electorate or changes in information acquisition do not drive this result. In addition, we provide evidence that rain might have altered the outcome of several high-stake votes. We discuss the psychological mechanism and document that rain reduces the willingness to take risks, a pattern that is consistent with the observed reduction in the support of change

P-values for high-dimensional regression

Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two parts. The number of variables is then reduced to a manageable size using the first split, while classical variable selection techniques can be applied to the remaining variables, using the data from the second split. This yields asymptotic error control under minimal conditions. It involves, however, a one-time random split of the data. Results are sensitive to this arbitrary choice: it amounts to a `p-value lottery' and makes it difficult to reproduce results. Here, we show that inference across multiple random splits can be aggregated, while keeping asymptotic control over the inclusion of noise variables. We show that the resulting p-values can be used for control of both family-wise error (FWER) and false discovery rate (FDR). In addition, the proposed aggregation is shown to improve power while reducing the number of falsely selected variables substantially.Comment: 25 pages, 4 figure