This research focuses on dimension-reduction techniques for modeling conditional extreme values. Specifically, we investigate the idea …

Sliced inverse regression (SIR) focuses on the relationship between a dependent variable $y$ and a $p$-dimensional explanatory variable …

The sensory and nutritional qualities of meats are strong expectations for consumers. However, these two types of quality are sometimes …

Since its introduction in the early 90’s, the Sliced Inverse Regression (SIR) methodology has evolved adapting to increasingly complex …

In the supervised high dimensional settings with a large number of variables and a low number of individuals, variable selection allows …

The ddsPLS method considers regression and classification problems in the context of multiblock structured covariate data sets taking …

For several years, studies conducted for discovering tenderness biomarkers have proposeda list of 20 candidates. The aim of the present …

The identification of novel biological factors associated with thrombin generation, a key biomarker of the coagulation process, remains …

Several sets of variables can be analyzed simultaneously by canonical correlation in a multi-way analysis. These sets of variables are …

Predicting vaccine efficacy remains a challenge. We used a systems vaccinology approach to identify early innate immune correlates of …

An interesting feature of Sliced Inverse Rregression (SIR) is that it allows the construction of indices, as linear combinations of the …

The problem of missing data often occurs in data analysis. Missing values of the type MAR (Missing At Random) are cosidered here. Then, …

The imputation is the process that estimates the missing values. Simplest approaches impute to fixed values such as mean/median based …

How can we deal with atypical observations in SIR regression. This can be generalized to any predictive model.

A reflexion about missing data imputation in the supervised context, with a solution and simulation results.

In recent years, data analysis methods have had to deal with new type of heterogeneous data sets. Multi-omics studies are perfect …

Sliced inverse regression (SIR) focuses on the relationship between a dependent variable $y$ and a $p$-dimensional explanatory variable …

The identification of novel biological factors associated with thrombin generation, a key biomarker of the coagulation process, remains …

Les récentes innovations techniques ont permis la production de données massives en biologie, comme les données omiques par exemple …

This is the last update of the current project on dealing with missing samples in multi-block context for supervised datasets.

*
#### How to deal with missing values, 2022

#### How to deal with missing values, 2021

#### Partial Least Squares (PLS), an introduction

#### Introduction à l'apprentissage statistique

#### Genetics project in the context of high dimensional data

#### Supervised and unsupervised analysis for high dimensinal data

#### PLS and sparse PLS

#### Maths and function analysis

#### Maths and linear algebra

#### Statistics and Probability

#### Single-cell datasets visualizations : PCA Versus tSNE

Some new things.

What are missing values and how to deal with them using R classical packages.

What is PLS and why ?

That course shows main goals of statistical learning detailing bias/variance tradeoff and most of my thesis results.

That course shows main goals of supervised and unsupervised methods to help students to prepare a project dealing with the 10X data set.

That course shows main goals of supervised and unsupervised methods and their applications through the spectrum of my proper applications.

Introduction to PLS (Partial Least Square) and one of its sparse versions.

Mathematical courses designed to fit to French second year medical students of the *Double Cursus Sante Sciences* expecting strong mathematical bases.

*Double Cursus Sante Sciences* expecting strong mathematical bases.

Mathematical courses designed to fit to second year medical students expecting strong mathematical bases.

Among new RNA sequencing methods, one permits to detail the RNA (or DNA) of a sample at a cellular level. That is the Single-Cell RNA-seq. That course explores methods to visualize those datasets.

Perform high dimensional analyses through statistical modeling keeping interpretability

Data analysis and eigen-space decomposition for supervised problems. Compressing many thoughts :

Missing Value

+High-dimension=Act carefully

Finite elements modelisation and real application to bridge movements with Fourier analysis. Great teams and amazing moments!

A team project dedicated to image segmentation using **SVM** tools and morphological transformation. A work done in cooperation with GE Healthcare and supervised by Arthur Tenenhaus and Laurent Le Brusquet.

Extra large images implying special dedicated tools such as spherical wavelets but also deeply open minds. What else to say ? It is hard!

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+++

widget = “about” active = true date = 2016-04-20T00:00:00

weight = 1

[interests] interests = [ “Statistical modelling”, “Missing Value Treatment”, “Variable selection”, “SIR”, “Bootstrap”, “Extreme Value Theory”, “Bayesian Statistics”, “PLS”, “Machine Learning”, “Teaching” ]

\[\[education.courses\]] course = “PhD in Biostatistics” institution = “Université de Bordeaux” year = 2019

\[\[education.courses\]] course = “Research Master, Applied Mathematics for Image and Signal Processing” institution = “CentraleSupelec and Paris-Sud University” year = 2015

+++

I am a Associate Professor at Aix-Marseille University
(**AMU**). I teach for all university degrees at the
**AMU**. My reserches are performed in the Statistical
group of the **ALEA
team**, one of the teams of the Institut de Mathématiques de
Marseille (**I2M**, UMR
7373).

I currently teach to

- Licence
Mathématiques et Informatique Appliquées aux Sciences Humaines et
Sociales (
**MIASHS**) - Master
Mathématiques Appliquées, Statistique (
**MAS**)

**I2M** is an Joint Research Unit (UMR in french) placed
under the triple tutelage of the CNRS, **AMU** and the
École Centrale de Marseille.

I unreasonably work on R and you can view the current stage on my
packages on **GitHub**. My wish
is to be useful to both of the communities which have so much to
share!

+++

widget = “about” active = true date = 2016-04-20T00:00:00

weight = 1

[interests] interests = [ “Statistical modelling”, “Missing Value Treatment”, “Variable selection”, “SIR”, “Bootstrap”, “Extreme Value Theory”, “Bayesian Statistics”, “PLS”, “Machine Learning”, “Teaching” ]

[education.courses] course = “PhD in Biostatistics” institution =“Université de Bordeaux” year = 2019

[education.courses] course = “Research Master, Applied Mathematics for Image and Signal Processing” institution = “CentraleSupelec and Paris-Sud University” year = 2015

+++

I am a Associate Professor at Aix-Marseille University (**AMU**). I
teach for all university degrees at the **AMU**. My reserches are
performed in the Statistical group of the **ALEA
team**,
one of the teams of the Institut de Mathématiques de Marseille
(**I2M**, UMR 7373).

I currently teach to

- Licence Mathématiques et Informatique Appliquées aux Sciences
Humaines et Sociales
(
**MIASHS**) - Master Mathématiques Appliquées, Statistique
(
**MAS**)

**I2M** is an Joint Research Unit (UMR in french) placed under the
triple tutelage of the CNRS, **AMU** and the École Centrale de
Marseille.

I unreasonably work on R and you can view the current stage on my
packages on **GitHub**. My wish is to be
useful to both of the communities which have so much to share!