Statistical Learning of Networks and Functional Data
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 2409
Special Issue Editor
Interests: nonparametric statistics; stochastic algorithms; statistics applied to life sciences; statistical learning of networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Databases are becoming more and more accessible, voluminous and complex. In order to make the best use of them, non-parametric statistical methods, stochastic algorithms, statistical learning of networks are frequently used.
There has been growing increasing interest in functional data analysis and statistical learning of networks, including correlation analyses for spatial and temporal data and classification techniques for complex data. Progress has often been driven by the application areas, such as neurosciences, environmetrics, chemometrics, biometrics, medicine, and econometrics.
The application of functional data and statistical learning of networks to data of real-world complex systems are often hindered by the frequent lack of the convergence problems and sufficient asymptotic mathematical properties. Contributions addressing any of these issues are very welcome.
This Special Issue aims to be a forum for the presentation of new and improved techniques in the area of functional data and statistical learning of networks. In particular, the analysis and interpretation of real-world natural and engineered complex systems with the help of non-parametric statistical methods, stochastic algorithms, statistical learning of networks, functional data fall within the scope of this Special Issue.
Prof. Dr. Yousri Slaoui
Guest Editor
Manuscript Submission Information
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Keywords
- nonparametric statistics
- stochastic algorithms
- statistics applied to life sciences
- statistical learning of networks
- artificial intelligence
