Addendum 2

Complementary courses open up more specialized areas of statistics and stochastic modeling. Their content may change from year to year. Topics may include
- 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 biomedical data, Reminders on survival data, competitive risk models, U-statistic-based testing, 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, etc.
- spatial statistics: Introduction to the fundamentals of spatial prediction and applications. In order to cover a wide range of spatial statistics, this course can be divided into two parts: point processes and geostatistics.
- linear mixed models : Extension of linear models to linear mixed models. Estimation of both fixed-effect and variance parameters within these models. Implementation on various practical cases. Random effects in generalized linear models.

Opening up 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

Timetable:
CM: 18h
TD:
TP:
Field :