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Section: New Software and Platforms
denoiseR
A R package that approximates a low-rank matrix from noisy data (Gaussian and Poisson Noise). Singular values shrinkage methods are implemented.
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
- Identifiability in mixed effects models
- Enhanced Method for Diagnosing Pharmacometric Models
- A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection
- Maximum likelihood estimation of a low-order building model
- LP-convergence of a Girsanov theorem based particle filter
- Adaptive estimation in the nonparametric random coefficients binary choice model by needlet thresholding
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
- Identifiability in mixed effects models
- Enhanced Method for Diagnosing Pharmacometric Models
- A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection
- Maximum likelihood estimation of a low-order building model
- LP-convergence of a Girsanov theorem based particle filter
- Adaptive estimation in the nonparametric random coefficients binary choice model by needlet thresholding
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography