SEMINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO https://www.carloalberto.org/events/category/seminars/seminars-in-statistics/page/2/?tribe-bar-date=2019-09-01

Venerdi 3 Marzo 2023, presso il Collegio Carlo Alberto, in Piazza Arbarello 8, Torino, si terranno i seguenti 2 seminari:

------------------------------------------------ Time: 11.00 - 12.00 Speaker: *Giovanni Rabitti* (Heriot Watt University, Edinburgh)

Title: *Fantastic sensitivity measures and where to find them*

Abstract: Data science and scientific modeling are extensively used to provide predictions for policy-making purposes. However, resulting communications need to be supported by a proper uncertainty quantification to assess variability in model predictions, by the identification of the key-uncertainty drivers. This information can be provided by statisticians with sensitivity analysis methods. Knowing the drivers of uncertainty supports effective policy-making. This talk is a guided tour in sensitivity analysis techniques and some recent results. In the first part of the talk, starting from the basics, local and global methods will be introduced. When the model is large (i.e. with many input variables), screening techniques are typically used to reduce the dimensionality of the scientific model and will be presented in the talk. In the second part of the talk, I will introduce cooperative games techniques to assess variable importance. I will present recent results on the computation on these importance measures, as well as their application to GAM models, to extreme value problems and to two actuarial pricing models. If there is time, I will introduce a game-theoretic interaction index for discovering subjects at higher identification risk.

------------------------------------------------ Time: 12.00 - 13.00 Speaker: *Thomas Berrett* (University of Warwick)

Title: *Optimal nonparametric testing of Missing Completely At Random, and its connections to compatibility*

Abstract: Given a set of incomplete observations, we study the nonparametric problem of testing whether data are Missing Completely At Random (MCAR). Our first contribution is to characterise precisely the set of alternatives that can be distinguished from the MCAR null hypothesis. This reveals interesting and novel links to the theory of Frechet classes (in particular, compatible distributions) and linear programming, that allow us to propose MCAR tests that are consistent against all detectable alternatives. We define an incompatibility index as a natural measure of ease of detectability, establish its key properties, and show how it can be computed exactly in some cases and bounded in others. Moreover, we prove that our tests can attain the minimax separation rate according to this measure, up to logarithmic factors. Our methodology does not require any complete cases to be effective, and is available in the R package MCARtest. ------------------------------------------------

Sarà possibile seguire entrambi i seminario anche in streaming: Join Zoom Meeting https://us02web.zoom.us/j/88246009718?pwd=UnVxL2R5RThwQllDbWVnbTdRNGVMUT09

Il seminario è organizzato dalla "de Castro" Statistics Initiative

www.carloalberto.org/stats