Extreme Values, Regular Variation and Empirical Processes

Dear Colleagues,

Extreme values of univariate, multivariate random variables and of stochastic process have soared to prominence over recent decades, notably being spurred on by a climate emergency. Regular Variation theory not only lies at the centre of classical Extreme Value theory, but it also continues to steadily underpin significant advances in statistical inference for tail-related data and connected empirical processes.

This Special Issue aims to celebrate, explore and disseminate the latest research taking place within the realms of Extreme Values, Regular Variation and Empirical Processes, including research developed at the interface of Extremes (broadly understood) with applied sciences at large. High-quality research contributions describing original, innovative, empirical, or scoping out-type unpublished work in the above-named areas, also mirrored in the following keywords, are cordially invited for publication:

Spatial and/or temporal process, methodological statistics, nonparametric statistics machine learning, causal inference, regression models, applied probability, graphical models, optimization, data science, applied statistical modeling and simulation.

Dr. Claudia Neves
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• spatial and/or temporal process
• methodological statistics
• nonparametric statistics machine learning
• causal inference
• regression models
• applied probability
• graphical models
• optimization
• data science
• applied statistical modelling and simulation