Statistics, Analytics, and Inferences for Discrete Data
A special issue of Stats (ISSN 2571-905X).
Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 24552
Special Issue Editor
Special Issue Information
Dear Colleagues,
Discrete data analysis concerns statistics, analytics, and inferences specifically designed for discretely measured data such as binary data, ordinal data, multinomial data, count data, and grouped continuous data. Discrete data are present in almost all research areas, and they are particularly common in biomedical sciences, social sciences, and business fields. The development of statistical tools for discrete data has played an instrumental role in drawing meaningful conclusions in clinical trials, oncology research, psychology studies, marketing, and e-commerce strategies. Due to the discrete nature of the data, however, fundamental statistical questions remain unresolved or not fully addressed. The lack of appropriate statistical tools is amplified by the advancement in information technology, social media, precision-X, and other emerging areas of practice and research.
This Special Issue calls for research papers devoted to the development of theories, methods, and applications for discrete data analysis. In particular, the authors are encouraged to develop the following:
- New analytic tools including measures, statistics, and visualization methods;
- New descriptive and predictive models as well as their assessment and diagnostics;
- Novel applications of existing methods to emerging problems in subject domains and industries;
- Reviews of current statistical practice in subject domains and industries with in-depth discussions on its limitations and pitfalls.
Dr. Dungang Liu
Guest Editor
Manuscript Submission Information
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