09:00 – 09:05 : Opening

09:05 - 09:30 : Morine DELHELLE
"Copula based dependent censoring in cure models"

Abstract :
In survival data analysis datasets with both a cure fraction (individuals who will never experience the event of interest) and dependent censoring (loss of follow-up for a reason linked to the event of interest before the occurence of this event) are not scarce and it is important to use an adequate model dealing with these two characteristics if we want to avoid bias in parameters estima-tions or false conclusions in clinical trials. In this presentation I will propose a fully parametric survival mixture cure model that takes possible dependent censoring into account which is based on an unknown copula that describes the relation between the survival and censoring times. So, the advantages of the model are that dependent censoring and the cure fraction are both consi-dered and that the copula is not assumed to be known. Moreover, it allows us to estimate the strength of dependence. The situations with and without covariates will be discussed. In survival data analysis datasets with both a cure fraction (individuals who will never experience the event of interest) and de-pendent censoring (loss of follow-up for a reason linked to the event of interest before the occurence of this event) are not scarce and it is important to use an adequate model dealing with these two characteristics if we want to avoid bias in parameters estimations or false conclusions in clinical trials. In this pre-sentation I will propose a fully parametric survival mixture cure model that takes possible dependent censoring into account which is based on an unknown copula that describes the relation between the survival and censoring times. So, the advantages of the model are that dependent censoring and the cure fraction are both considered and that the copula is not assumed to be known. Moreover, it allows us to estimate the strength of dependence. The situations with and without covariates will be discussed.

09:30 - 09:55 : Jean-loup DUPRET
"A Subdiffusive Stochastic Volatility Jump Model"

Abstract :
Subdiffusions appear as good candidates for modelling illiquidity in financial markets. Existing subdiffusive models of asset prices are indeed able to capture the motionless periods in the quotes of thinly-traded assets. However, they fail at reproducing simultaneously the jumps and the time-varying random vola-tility observed in the price of these assets. The aim of this work is hence to propose a new model of subdiffusive asset prices reproducing the main charac-teristics exhibited in illiquid markets. This is done by considering a stochastic volatility jump model, time-changed by an inverse subordinator. We derive the forward fractional partial differential equations (PDE) governing the probabi-lity density function of the introduced model and we prove that it leads to an arbitrage-free and incomplete market under a suitable change of measure. By proposing a new procedure for estimating the model parameters and using a series expansion for solving numerically the obtained fractional PDE, we are able to price various European-type derivatives on illiquid assets and to de-part from the common Markovian valuation setup. This way, we show that the introduced subdiffusive stochastic volatility jump model yields consistent and reliable results in illiquid markets.

09:55 - 10:20 : Hugues ANNOYE
"Generating administrative data bases with Wasserstein GAN"

Abstract :
In a world increasingly surrounded by data, data privacy and anonymisation are becoming more and more important. Under these circumstances, the need for fake data bases that replicate the characteristics of the population while preserving privacy is arising. In this presentation, we investigate how we can use Wasserstein generative adversarial network (WGAN), developed by [ACB17] in the context of image synthesis, to create administrative data bases and we also adapt it to take weights into account. Administrative data have the spe-cificity of mixing continuous and categorical data, which should be taken into account in the architecture of the WGANs. Then, we present a new method to evaluate the results based on Support Vector Data Description (SVDD) on real data coming from Labour Force Survey (LFS).


10:20 - 10:45 : Coffee break 


10:45 - 11:10 : Lise LEONARD
"High-dimensional regression : Model averaging and inference"

Abstract :
With the advent of technology and the proliferation of data collection me-thods, researchers now have access to vast amounts of data. High-dimensional regression models are designed to handle datasets with more predictors than observations, allowing researchers to leverage the wealth of information avai-lable in these high-dimensional datasets. However, these high-dimensional me-thods, such as the Lasso which is the most used in this context, depend on unknown tuning parameters. The goal of our proposal is to eliminate that difficult choice of the tuning parameter and obtain an estimator that allows inference. The main feature of our procedure is to pool together information from multiple estimators to obtain one single, final estimator. We propose a strategy to aggregate these regression coefficients to reduce the prediction risk of the estimation and to eliminate the tuning parameter. Theoretical results on the distribution and the prediction risk of the method are presented. In particular, we show the normality of the estimator even after the aggregation, which allows for inference for high-dimensional models. The performance of the method is illustrated by numerical simulations and an application on real data.

11:10 -  11:35 : Hortense DOMS
"Bayesian joint model for longitudinal HR-QOL and time-to-event outcomes"

Abstract :
In cancer clinical trials, the traditional endpoints are overall survival (OS) and progression-free survival (PFS). Recently, the use of health-related quality of life (HRQoL) as a major endpoint has become increasingly common. HRQoL data are measured using self-administered questionnaires collected longitudi-nally throughout the follow-up period and are used to assess changes in pa-tients’ perceptions of their physical and mental health over time. To analyse this data, the standard approach in oncology is to calculate a score for each patient at each time point and apply a linear mixed model (LMM) to the patient’s score. However, recent research has shown that using the LMM to analyse HRQoL is not appropriate. A more suitable methodology, known as item response theory (IRT), is therefore emerging. IRT models link patient responses to a latent variable representing the HRQoL dimension studied. Ad-ditionally, missing data in HRQoL questionnaires may result from dropouts due to clinical events. As this form of dropout can be informative, it is essen-tial to take it into account when analysing longitudinal outcomes in order to obtain valid results. In this presentation, we explain how to extend the joint model to the HRQoL longitudinal data framework. A multilevel item response theory model is used for longitudinal data and a proportional cause-specific hazards model is used for survival data. Inference is performed in a Bayesian framework using the Markov chain Monte Carlo algorithm and we apply the proposed model to data from patients with first progression of glioblastoma.

11:35 - 12:00 : Aigerim ZHUMAN
"Speeding up Monte Carlo Integration : Control Neighbors for Optimal Conver-gence"

Abstract :
The method of control variates is a powerful technique that allows to reduce the variance of the Monte Carlo estimate of a multivariate integral by introdu-cing auxiliary functions with known expectations, called control variates. We propose to use nearest neighbor estimates as control variates in order to speed up the convergence rate of the Monte Carlo integration procedure. Our novel estimate, called the Control Neighbor estimate, achieves the optimal conver-gence rate for Lipschitz functions. In addition, a non-asymptotic bound on the probabilistic error of the procedure is obtained via an extension of McDiarmi-d’s inequality for functions with bounded differences on a high probability set. Moreover, several numerical experiments confirm the good performance of the proposed estimate.