The 48th Statistical Machine Learning Seminar (online)

【Date & Time】
March 17, 2022 / 13:30-15:00

Admission Free

Please register at the following google form to receive the zoom link:
Benjamin Poignard (Graduate School of Economics, Osaka University)
Sparse M-estimator in semi-parametric copula models.
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 counterparts. It is well known that the latter modification significantly alters the limiting laws compared with usual M-estimation. We establish the consistency and the asymptotic normality of our sparse penalized M-estimator and we provide some sufficient conditions to get the asymptotic oracle property with pseudo-observations. Our assumptions allow us to manage copula based loss functions that are potentially unbounded. The numerical studies emphasize the relevance of the proposed sparse method in the context of model misspecification.

This is a joint work with J.D. Fermanian (ENSAE-CREST).

Research Center for Statistical Machine Learning, The Institute of Statistical Mathematics