Welcome

Course description

The objective of this course is to show students how statistics is used in practice to answer a specific question, by introducing a series of important model-based approaches.

The students will learn to select and use appropriate statistical methodologies and acquire solid and practical skills by working-out examples on real-world data sets from various areas including medicine, genomics, ecology, and others.

All analyses will be conducted mainly with the R software No strong knowledge neither of R is required (only basic scripting). You can find help easily on the web.

Important remark

Much of the material used in this course is due to Marc Lavielle, who was the first to set up the course. We only have made some adjustments to it.

Schedule (tentative)

Teachers : Julien Chiquet (lecture + PC1), Angélina Roche (PC2), Zacharie Naulet (PC3)

Course Evaluation: 1 group project (report + oral defense) + a final exam (moodle QCM with computer)

Course Language: French with all material in English

  1. Statistical tests (x2)

    • Two-populations comparison
    • Power analysis
    • Multiple Testing

  2. Regression models (x2)

    • Linear and Non Linear Regression models
    • Nonlinear regression models
    • Inference Diagnostic, Model comparison

  3. Mixed effects models (x2)

    • Linear mixed effects models
    • Nonlinear mixed effects models

  4. Mixture models and model-based clustering (x1)

    • Gaussian mixture models for data clustering

  5. Change-point detection (x1)

    • Dynamic programming
    • 1D signal segmentation