Journal of Data Science, Statistics, and Visualisation The journal welcomes contributions to practical aspects of data science, statistics and visualisation, and in particular those which are linking and integrating these subject areas. Papers should thus be oriented towards a very wide scientific audience, and can cover topics such as machine learning and statistical learning, the visualisation and verbalisation of data, big data infrastructures and analytics, interactive learning, advanced computing, and other important themes. JDSSV is an open access journal that charges no author fees. The journal now has a new review process aimed at reducing the turnaround time between initial submission and publication of accepted papers to three months.
- Graphical tools for visualizing cellwise and casewise outliers
by Mehdi Hirari, Mia Hubert, Peter Rousseeuw on 30 December, 2025Principal component analysis (PCA) and other dimension reduction methods can be affected by cellwise and casewise outliers. Several approaches have been […]
- Bridging the inference gap in multimodal variational autoencodersby Agathe Senellart, Stéphanie Allassonnière on 13 November, 2025
From medical diagnosis to autonomous vehicles, many critical applicationsrely on the integration of multiple heterogeneous data modalities. […]
- Kernel outlier detectionby Can Hakan Dagidir, Mia Hubert, Peter J. Rousseeuw on 27 June, 2025
new anomaly detection method called kernel outlier detection (KOD) is proposed.It is designed to address challenges of outlier detection in […]
- Visualizing distributions of covariance matricesby Tomoki Tokuda, Ben Goodrich, Iven Van Mechelen, Andrew Gelman, Francis Tuerlinckx on 10 June, 2025
Statistical graphics are generally designed for visualizing data, but in this case our primary goal is to understand complex multivariate models that might be […]
- Comparing model-based unconstrained ordination methods in the analysis of high-dimensional compositional count databy Wenqi Tang, Pekka Korhonen, Jenni Niku, Klaus Nordhausen, Sara Taskinen on 12 May, 2025
Model-based ordination of ecological community data has gained recently significant popularity among practitioners, largely due to increased availability and […]
- Sparse data-driven random projection in regression for high-dimensional databy Roman Parzer, Peter Filzmoser, Laura Vana-Gür on 9 May, 2025
We examine the linear regression problem in a challenging high-dimensional setting with correlated predictors where the degree of sparsity of the coefficients […]
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