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Differentially private methods for managing model uncertainty in linear regression models
(2025-03-01)
Article
Open AccessIn this article, we propose differentially private methods for hypothesis testing, model averaging, and model selection for normal linear models. We propose Bayesian methods based on mixtures of g-priors and non-Bayesian ... -
Differentially private hypothesis testing with the subsampled and aggregated randomized response mechanism
(2025)
Article
Open AccessRandomized response is one of the oldest and most well-known methods used to analyze confidential data. However, its utility for differentially private hypothesis testing is limited because it cannot simulaneously achieve ... -
Bayesian bootstraps for massive data
(2020-06-01)
Article
Open AccessIn this article, we present data-subsetting algorithms that allow for the approximate and scalable implementation of the Bayesian bootstrap. They are analogous to two existing algorithms in the frequentist literature: the ... -
Properties of the generalized inverse Gaussian with applications to Monte Carlo simulation and distribution function evaluation
(2025-05)
Article
Restricted access - publisher's policyWe introduce two mixture representations for the generalized inverse Gaussian (GIG) distribution. One mixture representation expresses the GIG as a continuous mixture of inverse Gaussians. The other reveals a relationship ... -
Learning undergraduate Engineering Fluid Mechanics course assisted by journal articles and research argumentation
(Tempus Publications, 2025-02-01)
Article
Restricted access - publisher's policyA new approach in engineering education encourages students to engage with scientific discovery by reading research articles rather than relying solely on textbooks. Although frequently overlooked in undergraduate programs, ... -
Selection of network parameters in direct ANN modeling of roughness obtained in FFF processes
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-01-06)
Article
Open AccessArtificial neural network (ANN) models have been used in the past to model surface roughness in manufacturing processes. Specifically, different parameters influence surface roughness in fused filament fabrication (FFF) ... -
Baleen stable isotopes reveal climate-driven behavioural shifts in North Atlantic fin whales
(Elsevier, 2024-12-10)
Article
Open AccessClimate variability impacts the structure and functioning of marine ecosystems and can trigger behavioural responses in organisms. We investigated whether such variability modulates diet and migration in the North Atlantic ... -
Features that influence bike sharing demand
(Elsevier Ltd, 2024-09-30)
Article
Open AccessDuring the last few years, Bike Sharing Systems (BSS) have become a popular means of transportation in several cities across the world, owing to their low costs and associated advantages. Citizens have adopted these systems ... -
The interannual variability of global burned area is mostly explained by climatic drivers
(American Geophysical Union (AGU), 2024-07-01)
Article
Open AccessBetter understanding how fires respond to climate variability is an issue of current interest in light of ongoing climate change. However, evaluating the global-scale temporal variability of fires in response to climate ... -
The ENBIS-22 quality and reliability engineering international special issue
(2023-12-17)
Article
Open Access -
Effectiveness of one and two doses of acellular pertussis vaccines against laboratory-confirmed pertussis requiring hospitalisation in infants: results of the PERTINENT sentinel surveillance system in six EU/EEA countries, December 2015 – December 2019
(2024-03)
Article
Open AccessBackground Monitoring effectiveness of pertussis vaccines is necessary to adapt vaccination strategies. PERTINENT, Pertussis in Infants European Network, is an active sentinel surveillance system implemented in 35 hospitals ... -
Multidimensional scaling for big data
(Springer, 2024-04-13)
Article
Open AccessWe present a set of algorithms implementing multidimensional scaling (MDS) for large data sets. MDS is a family of dimensionality reduction techniques using a n x n distance matrix as input, where n is the number of ...
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