3 citations · 3 across the 11 of their papers we have counts for
12 papers
Parametric estimation of Hawkes processes based on ordinary least squares
Benjamin Poignard, Yoann Potiron
We develop a parametric estimation framework for self-exciting Hawkes processes whose intensity functions admit a parametric form. The estimation procedure is based on ordinary lea…
Change-point detection in variance-covariance matrix
Ying Lin, Benjamin Poignard
We consider the joint estimation of change-point locations and the sparsity pattern of the variance-covariance matrix, which is assumed to evolve in a piecewise constant manner. By…
Estimation of time series by Maximum Mean Discrepancy
Pierre Alquier, Jean-David Fermanian, Benjamin Poignard
We define two minimum distance estimators for dependent data by minimizing some approximated Maximum Mean Discrepancy distances between the true empirical distribution of observati…
Sparse minimum Redundancy Maximum Relevance for feature selection
Peter Naylor, Benjamin Poignard, Héctor Climente-González +1
We propose a feature screening method that integrates both feature-feature and feature-target relationships. Inactive features are identified via a penalized minimum Redundancy Max…
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…
Factor multivariate stochastic volatility models of high dimension
Benjamin Poignard, Manabu Asai
Building upon factor decomposition to overcome the curse of dimensionality inherent in multivariate volatility processes, we develop a factor model-based multivariate stochastic vo…
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,…
High-Dimensional Sparse Multivariate Stochastic Volatility Models
Benjamin Poignard, Manabu Asai
Although multivariate stochastic volatility models usually produce more accurate forecasts compared to the MGARCH models, their estimation techniques such as Bayesian MCMC typicall…
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…