Statistical Analysis and Data Science for Complex Data, 2nd Edition
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 2380
Special Issue Editor
Interests: graphical models; high-dimensional data analysis; machine learning; measurement error and error classification; survival analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nowadays, thanks to the rapid development of technology, datasets can be collected easily in many fields, such as biology, manufacturing, etc. Typically, given a dataset, one may encounter situations wherein (i) the sample size is large or (ii) the dimension of variables is large, yielding so-called big data or high-dimensional data, respectively. However, rare samples or variables are informative in data analysis. On the other hand, datasets usually contain complex structures caused by the collection procedure, such as censoring, measurement errors, or missingness. With noisy data, it becomes more challenging to choose informative subdata, detect important variables, or conduct analyses. In light of these challenges, this Special Issue will provide a platform for publishing novel statistical methods and algorithms that handle these complex structures in various research fields. Topics of interest for this Special Issue include biostatistics, bioinformatics, causal inference, statistical process control, and survival analysis.
Dr. Li-pang Chen
Guest Editor
Manuscript Submission Information
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Keywords
- algorithm
- big data
- high dimensionality
- noisy data
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