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Statistical Learning & Inference Seminars

The seminars will take place fortnightly on Tuesdays 11am or 1pm.

Term 3, 23-24

Date, Time and Room


30/04, 11am, MS.02

Oliver Feng (Bath)

Optimal convex M-estimation via score matching


In the context of linear regression, we construct a data-driven convex loss function with respect to which empirical risk minimisation yields optimal asymptotic variance in the downstream estimation of the regression coefficients. Our semiparametric approach targets the best decreasing approximation of the derivative of the log-density of the noise distribution. At the population level, this fitting process is a nonparametric extension of score matching, corresponding to a log-concave projection of the noise distribution with respect to the Fisher divergence. The procedure is computationally efficient, and we prove that our procedure attains the minimal asymptotic covariance among all convex M-estimators. As an example of a non-log-concave setting, for Cauchy errors, the optimal convex loss function is Huber-like, and our procedure yields an asymptotic efficiency greater than 0.87 relative to the oracle maximum likelihood estimator of the regression coefficients that uses knowledge of this error distribution; in this sense, we obtain robustness without sacrificing much efficiency. Numerical experiments confirm the practical merits of our proposal. This is joint work with Yu-Chun Kao, Min Xu and Richard Samworth.

14/05, 11am, MS.01

Gilles Stupfler (University of Angers)

Some new perspectives on extremal regression


The objective of extremal regression is to estimate and infer quantities describing the tail of a conditional distribution. Examples of such quantities include quantiles and expectiles, and the regression version of the Expected Shortfall. Traditional regression estimators at the tails typically suffer from instability and inconsistency due to data sparseness, especially when the underlying conditional distributions are heavy-tailed. Existing approaches to extremal regression in the heavy-tailed case fall into two main categories: linear quantile regression approaches and, at the opposite, nonparametric approaches. They are also typically restricted to i.i.d. data-generating processes. I will here give an overview of a recent series of papers that discuss extremal regression methods in location-scale regression models (containing linear regression quantile models) and nonparametric regression models. Some key novel results include a general toolbox for extreme value estimation in the presence of random errors and joint asymptotic normality results for nonparametric extreme conditional quantile estimators constructed upon strongly mixing data. Joint work with A. Daouia, S. Girard, M. Oesting and A. Usseglio-Carleve.
04/06, 11am, MB0.07

Rebecca Lewis (Oxford)



18/06, 1pm, MB0.07

Jenny Wadsworth (Lancaster)

Inference for multivariate extremes


25/06, 11am, MB0.07