2022 Fiscal Year Annual Research Report
Parsimonious statistical modelling for high-dimensional problems
| Project/Area Number |
19K23193
|
|---|
| Research Institution | Osaka University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2019-08-30 – 2023-03-31
|
| Keywords | Asymptotic theory / Copulas / Factor models / Sparsity / Time series |
|---|
| Outline of Annual Research Achievements |
The research was dedicated to the high-dimensional modelling of multivariate models and the development of statistical methods to fix the so-called curse of dimensionality. We considered the sparse approach, which aims to improve the prediction accuracy and the precision of the estimators. A significant part of the research was devoted to the analysis of the theoretical properties of such sparsity-based techniques and to the assessment of their performances through simulated experiments and real data applications. For each of these models, we developed a sparse estimation framework that provides good theoretical properties (consistency, recovery of the sparse support), fast implementation algorithms and empirical results: high-dimensional random vectors could be specified in our sparse models and the empirical experiments emphasised the gain provided by the sparse approach compared to non-sparse methods.
|
