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pro vyhledávání: '"Poignard, Benjamin"'
We consider the problem of estimating a time-varying sparse precision matrix, which is assumed to evolve in a piece-wise constant manner. Building upon the Group Fused LASSO and LASSO penalty functions, we estimate both the network structure and the
Externí odkaz:
http://arxiv.org/abs/2410.04057
Autor:
Poignard, Benjamin, Asai, Manabu
Building upon the pertinence of the factor decomposition to break the curse of dimensionality inherent to multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework that relies on two viewpo
Externí odkaz:
http://arxiv.org/abs/2406.19033
Autor:
Poignard, Benjamin, Terada, Yoshikazu
We consider the estimation of factor model-based variance-covariance matrix when the factor loading matrix is assumed sparse. To do so, we rely on a system of penalized estimating functions to account for the identification issue of the factor loadin
Externí odkaz:
http://arxiv.org/abs/2307.05952
Autor:
Poignard, Benjamin, Asai, Manabu
Although multivariate stochastic volatility models usually produce more accurate forecasts compared to the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fast and effic
Externí odkaz:
http://arxiv.org/abs/2201.08584
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 the marginal distributions are unknown and replaced by their empirical
Externí odkaz:
http://arxiv.org/abs/2112.12351
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 been gaining much attention as well as post-selection inference. Inde
Externí odkaz:
http://arxiv.org/abs/2010.15659
Akademický článek
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Autor:
Poignard, Benjamin
Ce document traite du problème de la grande dimension dans des processus GARCH multivariés. L'auteur propose une nouvelle dynamique vine-GARCH pour des processus de corrélation paramétrisés par un graphe non dirigé appelé "vine". Cette approch
Externí odkaz:
http://www.theses.fr/2017PSLED010/document
Autor:
Poignard, Benjamin
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 restricted strong convexity of the unpenalized loss function, regular
Externí odkaz:
http://arxiv.org/abs/1808.05352
Autor:
Poignard, Benjamin
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, where both penalties of the SGL are weighted by preliminary random coeffi
Externí odkaz:
http://arxiv.org/abs/1611.06034