activity
20172026
most citedA Bayesian view of doubly robust causal inference

46 citations · 119 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ME2026

Choosing the threshold in extreme value analysis

Léo R. Belzile, Anthony C. Davison

One of the two dominant approaches for univariate extreme value analysis is to model exceedances above a large threshold, the choice of which has a large impact on inference and wh…

stat.CO2022★ 22 cited

A modeler's guide to extreme value software

Léo R. Belzile, Christophe Dutang, Paul J. Northrop +1

This review paper surveys recent development in software implementations for extreme value analyses since the publication of Stephenson and Gilleland (2006) and Gilleland et al. (2…

stat.AP2021★ 11 cited

Is there a cap on longevity? A statistical review

Léo R. Belzile, Anthony C. Davison, Jutta Gampe +2

There is sustained and widespread interest in understanding the limit, if any, to the human lifespan. Apart from its intrinsic and biological interest, changes in survival in old a…

stat.ME2020★ 4 cited

Improved inference on risk measures for univariate extremes

Léo R. Belzile, Anthony C. Davison

We discuss the use of likelihood asymptotics for inference on risk measures in univariate extreme value problems, focusing on estimation of high quantiles and similar summaries of…

stat.AP2020★ 24 cited

Human mortality at extreme age

Léo R. Belzile, Anthony C. Davison, Holger Rootzén +1

We use a combination of extreme value theory, survival analysis and computer-intensive methods to analyze the mortality of Italian and French semi-supercentenarians for whom there…

math.ST2017★ 12 cited

Extremal attractors of Liouville copulas

Léo R. Belzile, Johanna G. Nešlehová

Liouville copulas, which were introduced in McNeil and Neslehova (2010), are asymmetric generalizations of the ubiquitous Archimedean copula class. They are the dependence structur…

stat.ME2017★ 46 cited

A Bayesian view of doubly robust causal inference

Olli Saarela, Léo R. Belzile, David A. Stephens

In causal inference confounding may be controlled either through regression adjustment in an outcome model, or through propensity score adjustment or inverse probability of treatme…