3 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
Change Point Detection in Precision Matrices with D-trace Loss
Ying Lin, Benjamin Poignard, Ting Kei Pong +1
We consider the problem of estimating a time-varying sparse precision matrix, which is assumed to evolve in a piecewise constant manner. Building upon the Group Fused LASSO and LAS…
Sparse factor models of high dimension
Benjamin Poignard, Yoshikazu Terada
We consider the estimation of a sparse factor model where the factor loading matrix is assumed sparse. The estimation problem is reformulated as a penalized M-estimation criterion,…
Sparse M-estimators in semi-parametric copula models
Jean-David Fermanian, Benjamin Poignard
We study the large sample properties of sparse M-estimators in the presence of pseudo-observations. Our framework covers a broad class of semi-parametric copula models, for which t…
Post-selection inference with HSIC-Lasso
Tobias Freidling, Benjamin Poignard, Héctor Climente-González +1
Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning. Variable selection methods have thus…
Sparse Multivariate ARCH Models: Finite Sample Properties
Benjamin Poignard
We provide finite sample properties of sparse multivariate ARCH processes, where the linear representation of ARCH models allows for an ordinary least squares estimation. Under the…
Asymptotic Theory of the Sparse Group LASSO
Benjamin Poignard
This paper proposes a general framework for penalized convex empirical criteria and a new version of the Sparse-Group LASSO (SGL, Simon and al., 2013), called the adaptive SGL, whe…