1 citations · 1 across the 5 of their papers we have counts for
6 papers
Variable selection in a specific regression time series of counts
Marina Gomtsyan
Time series of counts occurring in various applications are often overdispersed, meaning their variance is much larger than the mean. This paper proposes a novel variable selection…
Sign-consistent estimation in a sparse Poisson model
Marina Gomtsyan, Céline Lévy-Leduc, Sarah Ouadah +1
In this work, we consider an estimation method in sparse Poisson models inspired by [1] and provide novel sign consistency results under mild conditions.
Variable selection in sparse multivariate GLARMA models: Application to germination control by environment
M. Gomtsyan, C. Lévy-Leduc, S. Ouadah +3
We propose a novel and efficient iterative two-stage variable selection approach for multivariate sparse GLARMA models, which can be used for modelling multivariate discrete-valued…
Variable selection in sparse GLARMA models
Marina Gomtsyan, Céline Lévy-Leduc, Sarah Ouadah +2
In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our ap…
Variable selection in sparse GLARMA models
M. Gomtsyan, C. Lévy-Leduc, S. Ouadah +1
In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our ap…
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension
Marina Gomtsyan, Nikita Mokrov, Maxim Panov +1
The existing approaches to intrinsic dimension estimation usually are not reliable when the data are nonlinearly embedded in the high dimensional space. In this work, we show that…