Additive quantile regression for clustered data with an application to children's physical activity

Additive models are flexible regression tools that handle linear as well as nonlinear terms. The latter are typically modelled via smoothing splines. Additive mixed models extend additive models to include random terms when the data are sampled according to cluster designs (e.g., longitudinal). These models find applications in the study of phenomena like growth, certain disease mechanisms and energy consumption in humans, when repeated measurements are available. In this paper, we propose a novel additive mixed model for quantile regression. Our methods are motivated by an application to physical activity based on a dataset with more than half million accelerometer measurements in children of the UK Millennium Cohort Study. In a simulation study, we assess the proposed methods against existing alternatives.Comment: 50 pages, 4 figures, 2 tables (18 supplementary tables

Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically "learn" models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on the learned model be more accurate than the estimation we could have obtained by sampling many system executions within the same amount of time? In this work, we investigate existing algorithms for learning probabilistic models for model checking, propose an evolution-based approach for better controlling the degree of generalization and conduct an empirical study in order to answer the questions. One of our findings is that the effectiveness of learning may sometimes be limited.Comment: 15 pages, plus 2 reference pages, accepted by FASE 2017 in ETAP

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Aspirin and clonidine in non-cardiac surgery: acute kidney injury substudy protocol of the Perioperative Ischaemic Evaluation (POISE) 2 randomised controlled trial

IntroductionPerioperative Ischaemic Evaluation-2 (POISE-2) is an international 2×2 factorial randomised controlled trial of low-dose aspirin versus placebo and low-dose clonidine versus placebo in patients who undergo non-cardiac surgery. Perioperative aspirin (and possibly clonidine) may reduce the risk of postoperative acute kidney injury (AKI).Methods and analysisAfter receipt of grant funding, serial postoperative serum creatinine measurements began to be recorded in consecutive patients enrolled at substudy participating centres. With respect to the study schedule, the last of over 6500 substudy patients from 82 centres in 21 countries were randomised in December 2013. The authors will use logistic regression to estimate the adjusted OR of AKI following surgery (compared with the preoperative serum creatinine value, a postoperative increase ≥26.5 μmol/L in the 2 days following surgery or an increase of ≥50% in the 7 days following surgery) comparing each intervention to placebo, and will report the adjusted relative risk reduction. Alternate definitions of AKI will also be considered, as will the outcome of AKI in subgroups defined by the presence of preoperative chronic kidney disease and preoperative chronic aspirin use. At the time of randomisation, a subpopulation agreed to a single measurement of serum creatinine between 3 and 12 months after surgery, and the authors will examine intervention effects on this outcome.Ethics and disseminationThe authors were competitively awarded a grant from the Canadian Institutes of Health Research for this POISE-2 AKI substudy. Ethics approval was obtained for additional kidney data collection in consecutive patients enrolled at participating centres, which first began for patients enrolled after January 2011. In patients who provided consent, the remaining longer term serum creatinine data will be collected throughout 2014. The results of this study will be reported no later than 2015.Clinical Trial Registration NumberNCT01082874

Acquisition of mine waste from closed deposition facilities of high natural value

W artykule przedstawiono rozwiązania pozwalające na prowadzenie wydobycia odpadów ze wzbogacania rud miedzi z nieeksploatowanego obiektu odpadów wydobywczych z zachowaniem cennych przyrodniczo stanówisk bytowania chronionych gatunków zwierząt. Prezentowany projekt jest przykładem prowadzenia działalności przemysłowej na terenach obiektów objętych sukcesją naturalną, związanych z przemysłem wydobywczym, zgodnie z zasadami zrównoważonego rozwoju.This paper describes solutions of sustainable extraction of copper ore processing waste in the area of inoperative mining waste facility, simultaneously preserving valuable habitats of legaly protected animals. The presented project illustrates the way of running business activity in mining areas covered by natural succession, in accordance with the principles of sustainable development