Publications des institutions partenaires
A computationally efficient framework for automatic inertial sensor calibration
The calibration of (low-cost) inertial sensors has become increasingly important over the past years since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3D-animation. However, this calibration procedure is often quite problematic since the signals issued from these sensors have a complex spectral structure and the methods…
Institution partenaire
English / 01/01/2018
Effects of substance use disorder on treatment process and outcome in a ten-session psychiatric treatment for borderline personality disorder
Institution partenaire
English / 01/01/2018
Prediction Divergence Criterion for Model Selection in the Logistic Regression
In this Master Thesis, we have analytically derived and numerically implemented three estimators of the Prediction Divergence Criterion (Avella-Medina et al., working paper) for Model Selection within the logistic regression framework. After the validation of these estimators by means of simulations, we have performed Model Selection both when the order of the variables was known in…
Institution partenaire
English / 01/01/2018
Validity and accuracy of posterior distributions in Bayesian statistics
In this thesis I investigate the validity and the accuracy properties of the posterior quantiles in Bayesian statistics when replacing the parametric likelihood with the Cressie-Read empirical likelihoods based on a set of unbiased M-estimating equations. At first order I study the validity of the empirical posterior distribution derived from the pseudo-likelihood constructed with…
Institution partenaire
English / 01/01/2018
Bootstrap estimation of uncertainty in prediction for generalized linear mixed models
In the framework of Mixed Models, it is often of interest to provide an es- timate of the uncertainty in predictions for the random effects, customarily defined by the Mean Squared Error of Prediction (MSEP). To address this computation in the Generalized Linear Mixed Model (GLMM) context, a non-parametric Bootstrap algorithm is proposed. First, a newly developed Bootstrap scheme…
Institution partenaire
English / 01/01/2017
A random-effects hurdle model for predicting bycatch of endangered marine species
Understanding and reducing the incidence of accidental bycatch, particularly for vulnerable species such as sharks, is a major challenge for contemporary fisheries management worldwide. Bycatch data, most often collected by at-sea observers during fishing trips, are clustered by trip and/or vessel and typically involve a large number of zero counts and very few positive counts.…
Institution partenaire
English / 01/01/2017
Established risk factors for addiction fail to discriminate between healthy gamers and gamers endorsing DSM-5 Internet gaming disorder
Background and aims The DSM-5 includes criteria for diagnosing Internet gaming disorder (IGD) that are adapted from substance abuse and widely used in research and clinical contexts, although evidence supporting their validity remains scarce. This study compared online gamers who do or do not endorse IGD criteria regarding self-control-related abilities (impulsivity, inhibitory…
Institution partenaire
English / 01/01/2017
On the Inference of Random Effects in Generalized Linear Mixed Models
In the first chapter, the problem of Bootstrap inference for the parameters of a GLMM is addressed. We formulate a bootstrapping strategy consisting on the random weighting of the contributions to the Joint Likelihood of Outcomes and Random Effects. Using the Laplace Approximation method for integrals on this function, yields a Random Weighted Log-Likelihood that produces the desired…
Institution partenaire
English / 01/01/2017
Confidence sets for model selection
At first glance the goals of model selection might seem clear. Out of a set of possible models, we want to select the ”best” or a subset of ”best” models. This notion of ”best” however is not well defined, since it obviously depends on the initial goals of the selection. In order to study the uncertainty in model selection, we introduce a new definition of a model, where the models…
Institution partenaire
English / 01/01/2016
Robust inference for random fields and latent models
This thesis delivers a new framework for the robust parametric estimation of random fields and latent models through the use of the wavelet variance. By proposing a new M-estimation approach for the latter quantity and delivering results on the identifiability of a wide class of latent models, the thesis finally delivers a computationally efficient and statistically sound method to…
Institution partenaire
English / 01/01/2016
Measures of model adequacy and model selection in mixed-effects models
This thesis contributes to the development of measures of model selection and model adequacy for mixed-effects models. In the context of linear mixed-effects models, we review and compare in a simulation study a large set of measures proposed to evaluate model adequacy or/and to perform model selection. In the more general context of generalized linear mixed-effects models, we…
Institution partenaire
English / 01/01/2016
Robust penalized M-estimators for generalized linear and additive models
Generalized linear models (GLM) and generalized additive models (GAM) are popular statistical methods for modelling continuous and discrete data both parametrically and nonparametrically. In this general framework, we consider the problem of variable selection by studying a wide class of penalized M-estimators that are particularly well suited for high dimensional scenarios where the…
Institution partenaire
English / 01/01/2016
Capitalizing upon the attractive and addictive properties of massively multiplayer online role-playing games to promote wellbeing
Institution partenaire
English / 01/01/2016
A predictive based regression algorithm for gene network selection
Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. To do so, many of the recently proposed classification methods require some form of dimension-reduction of the problem which finally provide a single model as an output and,…
Institution partenaire
English / 01/01/2016
Time-frequency Granger causality with application to nonstationary brain signals
This PhD thesis concerns the modelling of time-varying causal relationships between two signals, with a focus on signals measuring neural activities. The ability to compute a dynamic and frequency-specific causality statistic in this context is essential and Granger causality provides a natural statistical tool. In Chapter 1 we propose a review of the existing methods allowing one to…
Institution partenaire
English / 01/01/2015
Robust Inference for Time Series Models: a Wavelet-Based Framework
We present a new framework for the robust estimation of time series models which is fairly general and, for example, covers models going from ARMA to state-space models. This approach provides estimators which are (i) consistent and asymptotically normally distributed, (ii) applicable to a broad spectrum of time series models, (iii) straightforward to implement and (iv)…
Institution partenaire
English / 01/01/2015
Simulation based bias correction methods for complex problems
Nowadays, the increase in data size and model complexity has led to increasingly difficult estimation problems. The numerical aspects of the estimation procedure can indeed be very challenging. To solve these estimation problems, approximate methods such as pseudo-likelihood functions or approximated estimating equations can be used as these methods are typically easier to implement…
Institution partenaire
English / 01/01/2015
Prevalence and characteristics of addictive behaviors in a community sample: A latent class analysis
While addictions to substances such as alcohol, tobacco, and other drugs have been extensively investigated, interest has been growing in potential non-substance-related addictive behaviors (e.g., excessive gambling, buying or playing video games). In the current study, we sought to determine the prevalence and characteristics of a wide range of addictive behaviors in a general…
Institution partenaire
English / 01/01/2015
Estimating the number of garment factories in Bangladesh
Institution partenaire
English / 01/01/2015
Influence functions for penalized M-estimators
Institution partenaire
English / 01/01/2014
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