Supplement 1

Complementary courses present openings to more specialized areas of statistics and stochastic modeling. Their content may change from year to year. The topics covered may be the following
- biological sequence analysis: probabilistic models of biological sequence evolution, phylogeny inference, hidden Markov models for pattern detection, graphical models and inference of gene regulation networks
- population dynamics: birth and death processes (definitions, properties, asymptotic behavior, parameter estimation, simulation), deterministic, stochastic or hybrid approximations
- biomedical statistics: Introduction to clinical research data, regulatory and methodological aspects, Likelihood function and applications to bio-medical data, Reminders on survival data, competitive risk models, test based on a U-statistic, Models for fertility data analysis, Medical diagnosis and ROC curves as an application of a U-statistic, Meta-analyses.
- Extreme value statistics and environmental applications: Univariate and multivariate extreme value theory: law of maxima and high threshold violations for random variables and vectors, extreme dependencies, estimation of extreme quantiles, risk studies. Applications to environmental data: rainfall, wave heights, temperatures...
- Spatial statistics: Introduction to the fundamentals of spatial prediction and applications. In order to cover a wide range of spatial statistics, this course will be divided into two parts: point processes and geostatistics.
- linear mixed models : Extension of linear models to linear mixed models. Estimation of fixed effect parameters as well as variance parameters within these models. Implementation on different practical cases. Random effects in generalized linear models.

Opening to more specialized fields of statistics and stochastic modeling

 M1 SSD or equivalent, M2 SSD first semester courses



Recommended prerequisites: M1 SSD or equivalent, M2 SSD first semester courses

Hourly volumes:
CM: 18h
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