Poignard, Benjamin
Faculty of Science and Technology Dept. Mathematics Associate Professor
Graduate School of Science and Technology School of Mathematical and Physical Sciences Curriculum of Mathematics and Mathematical Sciences Associate Professor
Research Overview
The research is dedicated to the sparse statistical modeling for multivariate random models. It aims to solve the curse of dimensionality problem, that is the explosive number of parameters, inherent to most multivariate models by fostering parsimony among the parameters. The following points are considered: - the specification of relevant sparsity-based techniques; - the theoretical analysis of the sparsity-based estimators (asymptotic properties, finite sample); - the implementation of efficient optimization procedures.
Specialty
Statistics, Econometrics, Multivariate time series, Sparse modeling, Machine learning
Thesis Guide Qualification
Thesis Guide Qualification in the Graduate School of Science and Technology
Master/Doctor