7 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…