Compositional Data Analysis in the Data Science Era
A special issue of Stats (ISSN 2571-905X).
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 166
Special Issue Editors
Interests: compositional data; distributions on the simplex; robust statistics; statistical methods for ecological data
Special Issue Information
Dear Colleagues,
We are living in the era of data science, advanced analytics, and big data, which stimulate and prompt the progress in and evolution of almost every aspect of our lives.
Data-oriented approaches have undoubtedly brought about a lot of transformation in the fields of machine learning and deep learning. This process of evolution has occurred in several fields, ranging from geology, biology, molecular biosciences, environmental sciences, and ecology to forensic sciences, medicine, hydrology, bioinformatics, and economics.
Compositional data, namely data where the relevant information is contained in the ratios between their components or parts, and in general constrained data sets, are ubiquitous in the above-mentioned fields.
Subsequently, the need for statistical and computational methods, as well as machine learning algorithms, that consider the special nature of compositional data has increased.
This Special Issue aims to provide an overview of ongoing research in compositional data analysis. The novelty of contributions may lie in either the methodologies employed or the unique and innovative application of such methodologies to this kind of data that provides new and significant empirical insights.
Topics of interest include regression, time series, classification, and variable selection algorithms for long and/or wide-formatted compositional data.
Dr. Gianna S. Monti
Dr. Michail Tsagris
Guest Editors
Manuscript Submission Information
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Keywords
- compositional data analysis
- simplex
- transformations
- multivariate analysis
- count data
- machine learning
- sparse alternative
- model selection
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