Integrative Approaches in Statistical Modeling and Machine Learning for Data Analytics and Data Mining
A special issue of Stats (ISSN 2571-905X).
Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 221
Special Issue Editors
Interests: machine/statistical learning and modeling; clustering, PLS, and EM algorithm; artificial intelligence, big data, data mining, and data science; influence diagnostics
Special Issues, Collections and Topics in MDPI journals
Interests: bayesian modeling; epidemiological models; fuzzy logic and decision-making; machine learning and data analytics; nonparametric inference; optimization techniques
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of statistical methods with advanced computational techniques is a rapidly growing field, transforming how we extract insights from large and complex datasets. This synergy is crucial for addressing multifaceted challenges across various scientific and industrial domains. As data volumes grow exponentially, the need for robust statistical models and efficient computational algorithms to process and interpret this information becomes increasingly important. The application of these methodologies is driving innovations in areas such as healthcare, finance, and environmental science, underscoring the relevance of this research area.
The primary aim of this Special Issue is to promote the convergence between statistical modelling and advanced computational techniques, focusing on their application to data-driven discovery. By fostering a deeper understanding and development of these statistical and computational methods, we aim to push the boundaries of current knowledge and practice, contributing to the advancement of science and technology.
We invite submissions that explore the innovative intersection of statistical methodologies and computational techniques. Contributions may include original research articles, review papers, case studies, and methodological papers. Suggested themes for submission include, but are not limited to, the following:
- Big data analytics;
- Biostatistics;
- Data science;
- Machine learning;
- Multivariate analysis;
- Predictive analytics;
- Spatial modelling;
- Statistical learning;
- Statistical modeling;
- Survival analysis.
Dr. Victor Leiva
Dr. Cecília Castro
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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. Stats is an international peer-reviewed open access quarterly 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 1600 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.
Keywords
- artificial intelligence
- big data analytics
- computational techniques
- data integration
- data mining
- deep learning
- machine learning
- predictive modelling
- robust algorithms
- statistical methods
- statistical modeling
- synergy in data analysis
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