Patrimony

The Louis Bachelier Group's patrimony has been defined as all the publications produced by academic researchers thanks to Group funding (ILB, FdR, IEF, Labex) or via the use of EquipEx data (BEDOFIH, EUROFIDAI).


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Estimation of the number of factors in a multi-factorial Heath-Jarrow-Morton model in electricity markets.

Electricity markets, Model calibration, Model selection, Power price model

Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics.

Adaptive LASSO, General-to-specific, Genetic data, Model selection, Monte Carlo simulation, Sparse models

Efficiency of the V -Fold Model Selection for Localized Bases.

Heteroscedastic noise, Model selection, Nonparametric regression, Random design, V-fold cross-validation, V-fold penalization, Wavelets

Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases.

Cross-validation, Heteroscedastic noise, Model selection, Nonparametric regression, Random design, Wavelets

Optimal selection of localized basis models in heteroskedastic regression.

Cross-validation, Heteroscedastic noise, Localised basis, Model selection, Nonparametric regression, Oracle inequality, Slope heuristics, Wavelets

A theoretically founded over-penalization of the AIC criterion.

Akaike's information criterion, Density estimation, Histogram, Model selection, Over-penalisation

Nonparametric drift estimation for diffusions with jumps driven by a Hawkes process.

60J60, 60J75, Diffusion, Hawkes process, Model selection, Nonparametric estimator, Nonparametric estimator Model selection Diffusion Hawkes process AMS Classification 62G05

New adaptive strategies for nonparametric estimation in linear mixed models.

Deconvolution, Linear mixed models, Model selection, Nonparametric estimation

Nonparametric drift estimation for diffusions with jumps driven by a Hawkes process.

60J60, 60J75, Diffusion, Hawkes process, Model selection, Nonparametric estimator, Nonparametric estimator Model selection Diffusion Hawkes process AMS Classification 62G05

Mixtures of GLMs and number of components: application to surrender risk in life insurance.

Classification, Comportement de rachat, Finite mixtures, GLM, Model selection, Mélange, Surrender behaviour, Sélection de modèle

Mixtures of GLMs and number of components: application to surrender risk in life insurance.

Classification, Comportement de rachat, Finite mixtures, GLM, Model selection, Mélange, Surrender behaviour, Sélection de modèle

Tree-based censored regression with applications in insurance.

CART, Censoring, Insurance, Model selection, Regression tree, Survival analysis