A high-dimensional Wilks phenomenon

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Finite Sample Bernstein -- von Mises Theorem for Semiparametric Problems (2013, methodology): “f notions, methods and tools in the classical theory are formulated in the asymptotic setup with the growing sample size. Only few nite sample general results are available; see e.g. the recent paper Boucheron and Massart (2011). This paper focuses on the semiparametric problem when the full paramet”
Bernstein - von Mises Theorem for growing parameter dimension (2013, background): “ymptotic considerations. Any ﬁnite sample approach requires to change most of mathematical devices. There are only few general results in statistical inference which are stated for ﬁnite samples; see Boucheron and Massart (2011) and references therein in context of i.i.d. modeling. This paper adopts”
A modern maximum-likelihood theory for high-dimensional logistic regression (2018, background): “Such results have further motivated [17] to investigate the non-asymptotic Wilks’ phenomenon or, more precisely, the concentration behavior of the difference between the excess empirical risk and the true risk, from a statistical learning theory perspective.”
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance (2019, unknown): “[7] Stéphane Boucheron and Pascal Massart.”
Critical dimension in the semiparametric Bernstein—von Mises theorem (2014, unknown): “, the recent paper [6]).”