FW-B (Federation Wallonie-Bruxelles) 2020-2025
Imperfect Data : From Mathematical Foundations to Applications in Life Sciences (IMAL)
We are witnessing a period of time where the data collection potential has increased exponentially. The cautionary tale of this big data era is that large amounts of data do not necessarily contribute to an increment in our knowledge about the underlying phenomenon. One of the principal reasons for this is that even though one would desire to measure a characteristic for a subject, in many instances one can only get an approximate measurement due to difficulty in obtaining the direct measurement of the desired phenomenon (e.g. tumor size), non-replicability across instances (e.g. blood pressure), necessity to obtain numerous measurements rapidly, sometimes at the cost of accuracy. As a result, many modern observed markers are proxies for the real data because invasive, costly or too complex methods would be required to obtain accurate measurements. In this project we study how one can correct for different types of imperfect data when building statistical models with a focus on applications coming from life sciences. Imperfect data appear in different contexts, structures and models, and this project focuses on two common settings which regularly suffer from imperfect data: data in a regression context with imperfectly measured explanatory variables (Theme 1) and highdimensional or functional data with measurement error (Theme 2).
Promoters : Catherine Legrand (porte-parole, UCLouvain), Anouar El Ghouch (UCLouvain), Philippe Lambert (UCLouvain / ULiège),
Eugen Pircalabelu (UCLouvain), Germain Van Bever (UNamur), Ingrid Van Keilegom (UCLouvain / KU Leuven).
RW (Région Wallonne) 2019-2021
Using biomarkers to enrich interim analyses in cancer clinical trials
Subvention FIRST Entreprise Docteur de la Région Wallone
Promotor: Catherine Legrand
FW-B (Federation Wallonie-Bruxelles) 2018-2023
Sustainable, Adequate and Safe Pensions
This interdisciplinary research project (law, economics, actuarial science, philosophy) aims at critically assessing the key conditions that a public pension system should fulfil to be successfully reformed. Our hypothesis is that there are three such conditions: i) financial sustainability, ii) social adequacy and iii) safe governance. Hence, the ‘SAS’ acronym. Our goal is to identify the pension architecture that is the most likely to generate SAS pensions.
Promoters : Pierre Devolder, Alexia Autenne, Jean Hindriks, Vincent Vandenberghe, Axel Gosseries (ARC project)
Website : https://saspensions.wordpress.com/
FW-B (Federation Wallonie-Bruxelles) 2018-2023
Negative and ultra-low interest rates: behavioral and quantitative modelling
Interest rates are a cornerstone of economics and finance. They are at the foundation of asset pricing and monetary policy, and more generally of all intertemporal choices made by market participants and institutions every day, with huge consequences for the economic activity and wellbeing of our societies. Until recently, it was assumed (mostly implicitly) that interest rates could only possibly be positive. Notwithstanding, in the wake of the financial crisis initiated in 2008, major central banks of developed countries have been brought to conduct rates policies that turned them negative. The consequences of such a paradigm shift are both potentially huge and not well understood yet. This research project aims at shedding light on these consequences, both from an academic and a policy viewpoint, following three intertwined research lines that bring together a multidisciplinary team of researchers working on Behavioral Finance, Macro Finance, and Quantitative Finance.
Promoters: Catherine D’Hondt, Julio Dávila, Leonardo Iania, Christian Hafner, Olivier Corneille and Frederic Vrins.
RW (Région wallonne) 2018-2021
The BeNeFit project is a collaborative effort between UCLouvain (Main promotor: Catherine Legrand, ISBA-LIDAM), two private industry (IDDI, BMS), one non-profit organization (EORTC, via an Innoviris funding) and the Hopitaux Universitaires de Lyon.
This project, entitled "Biostatistical Estimation of Net Effects for Individualization of Therapy” obtained a Biowin grant (Pôle de compétitivité Région Wallone). The objective is to develop a new method to combine information about the different aspects of a new therapy (short and long term outcome, toxicity, quality of life) rather than focusing on an primary endoint as is currently done in clinical research. This new statistical methodology, based on further developments of “generalized pairwise comparisons”, will also be implemented in an appropriate software to allow the analysis of the data from a clinical trial while choosing a hierarchy in these outcomes, based on the priority of each patient and medical doctor to provide a specific answer in terms of risk-benefit.
Promoter : Catherine Legrand
SAS Partnership 2018-2022
The SAS software is one of the most used statistical software in the world. Since several years, there exist a partenariat between SAS and Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA) through which courses of programming in SAS and data mining techniques are organized. These courses are open to all master students as well as to PhD students and to all researchers of the UCLouvain. Within the context of this partenariat, SAS also support (financially and logistically) the organisation of short courses within ISBA.
Promoter: Catherine Legrand
BSP (Belgian Science Policy) 2016-2021
BRAIN-be : Valorisation de 70 ans d'observations soclaires de l'Observatoire Royal de Belgique
This is an interdisciplinary project (VAL-U-SUN) between the Solar Influence Data Analysis Center of the Royal Observatory Belgium (Uccle, Brussels), Drs Laure Lefevre et Veronique Delouille, and l’ISBA, on the analysis of the international Sunspot Index. In a four years’ PhD project, the task is to statistically characterise the sunpot data set, by developing a model for its short and long term behaviour over time, including its statistical uncertainties. The overall goal is to come up with an automated quality control algorithm that allows (almost) online supervision of the evolution of the data reported by a collection of sunspot observation station around the world that contribute to establishing a statistically “clean” index. Assistance to this project is given by the SMCS (Christian Ritter).
Promoter: Rainer von Sachs, Researcher: Sophie Mathieu
FNRS CDR 2019-2021
Optimal transport in nonparametric statics: Copulas for non-Euclidean data and multivariate tail quantile countours
The aim is to explore the potential of concepts and methods from the theory of optimal measure transport for statistical modelling and inference. We will extend the classical probability integral and quantile transforms on the real line to more general state spaces thanks to cost-minimizing mappings pushing a reference measure towards a target measure. Based on these transforms, we will seek to extend Sklar's celebrated copula theorem to more general metric spaces. The unit circle will serve as test case, and will allow us to model dependence between directional data. Further, we will study properties of tail quantile contours of multivariate regularly varying distributions defined via cyclically monotone mappings. Such contours are expected to satisfy a shape constraint involving a compact convex body, and we will look to exploit this information to construct efficient estimators.
Promoter: Johan Segers
Partnership KULeuven - UCLouvain 2020-2024
Quantile regression for censored data
One of the statistical challenges in survival analysis is the study of the relationship between a time-to-event response T and a set covariates X. This can be done using a wide variety of regression techniques like, for example, linear, AFT or Cox models. A robust and flexible alternative to these classical models is quantile regression, which has gained considerable popularity and interest in recent years. Many methods have been developed for quantile regression with completely observed data. But when data are subject to censoring, statistical estimation and inference become more difficult, and the literature is sparse. The existing work focuses on the case of i.i.d. data with a right-censored response, but in practice censoring mechanisms can be quite complicated (e.g. interval censoring) and may concern both the response and the covariates. The objective of this project is to develop and study consistent and computationally efficient procedures to conduct estimation and inference in quantile regression models with complicated censoring mechanisms. To this end, an enriched asymmetric Laplace distribution will be proposed and studied. Once studied, this distribution will be used to investigate the case of quantile regression with (1) censored response, (2) censored covariates and (3) censored response and censored covariates.
Promoter: Anouar El Ghouch & Ingrid Van Keilegom
ETHIAS Chair 2019-2022
Fully funded Pension Systems
The purpose of this interdisciplinary research project (law, actuarial science) is to look at the future of fully funded occupational pension schemes in the context of ageing and low interest rates.
Promoter: Pierre Devolder
AG Insurance Chair 2016-2020
Pension valuation and solvency
Development of a coherent and universal model of valuation and solvency requirement of pension liabilities for pension funds and insurance companies in a stochastic environment.