Statistical Inference and Learning Methods for Structured and GraphBased Data with Applications in Engineering and Science
Scientific-Disciplinary Group
13/STAT-01 - Statistics
Description
The project develops a family of statistical inference and learning methods for structured and graphbased data. Building on composite likelihood theory and robust estimation theory, it aims to design flexible and scalable frameworks for sparse estimating equations that capture complex dependence structures through local model components. The research introduces general principles for selecting informative equations to balance efficiency and interpretability, extending likelihood-based inference to large-scale systems where the full likelihood is intractable. Further developments address robust estimation, ensuring stability under model misspecification, noise, or heavy-tailed distributions... (see call)
Working time
Other
Number of positions
1
How to apply
View the original posting on the MUR website: Go to MUR website