Échantillonnage, protocole de collecte et impacts sur la mesure des violences
International audience
On the quantification of discretization uncertainty: comparison of two paradigms
Numerical models based on partial differential equations (PDE), or integro-differential equations, are ubiquitous in engineering and science, making it possible to understand or design systems for which physical experiments would be expensive—sometimes impossible—to carry out. Such models usually construct an approximate solution of the underlying ...
A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classificat...
We propose a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted ...
A constrained marginal zero-inflated binomial regression model
Zero-inflated models have become a popular tool for assessing the relationships between explanatory variables and a zero-inflated count outcome. In these models, regression coefficients have latent class interpretations, where the latent classes correspond to a susceptible subpopulation with observations generated from a count distribution and a no...
Reject Inference Methods in Credit Scoring: A rational review
The granting process of all credit institutions is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability also called score is learnt based on a dataset in which rejected applicants are de facto excluded. This implies that the population on which the score is used will be different from ...
Fast Incremental Expectation Maximization for non-convex finite-sum optimization: non asymptotic convergence bounds *
Fast Incremental Expectation Maximization was introduced to design Expectation-Maximization (EM) for the large scale learning framework involving finite-sum and possibly non-convex optimization. In this paper, we first recast this iterative algorithm and other incremental EM type algorithms in the Stochastic Approximation within EM framework. Then,...
Mixture of hidden Markov models for accelerometer data
Motivated by the analysis of accelerometer data, we introduce a specific finite mixture of hidden Markov models with particular characteristics that adapt well to the specific nature of this type of data. Our model allows for the computation of statistics that characterize the physical activity of a subject (\emph{e.g.}, the mean time spent at diff...
Optimising criteria for manual smear review following automated blood count analysis: A machine learning approach
The complete blood count (CBC) performed by automated haematology analysers is the most common clinical procedure in the world. Used for health checkup, diagnosis and patient follow-up, the CBC impacts the majority of medical decisions. If the analysis does not fit an expected setting, the laboratory staff manually reviews a blood smear, which is h...
Statistical power: implications for planning MEG studies
Nonparametric estimation in a regression model with additive and multiplicative noise
In this paper, we consider an unknown functional estimation problem in a general nonparametric regression model with the feature of having both multiplicative and additive noise.We propose two new wavelet estimators in this general context. We prove that they achieve fast convergence rates under the mean integrated square error over Besov spaces. T...
