Topics
Due to the high complexity and dimension of the real world, the processing of data requires tools and methods capable of helping decision making.
The aim of this workshop is to bring together scientists working in applied statistics, scientific computation and applications in all areas of sciences involving a great volume of data or special datasets such as engineering, industry, economics, life sciences and social sciences.
The main areas cover:
Computational Statistics - new issues in the design of computational algorithms for implementing statistical methods, machine learning, development in R, Python, etc
Applications - statistical case study in all areas of sciences, engineering and industry, including economics, medicine, biology, earth sciences and social sciences.
Topics of interest include but are not limited to:
- Statistical Inference
- Statistical computing
- Machine Learning
- Biostatistics
- Reliability
- Survival analysis
- Industrial Statistics
- Decision Theory
- Design of Experiments
- Multivariate Analysis
- Non parametric Inference
- Statistical Genetics
- Statistical Quality Control
- Survey Sampling
- Computational Bayesian methods.