This is a grade course on robust statistics and optimization.
WARNING: Useful code (for plotting) for the entire notebooks in this course utils.py
Syllabus:
Cours:
Introduction: IntroRobustness.pdf; IntroRobustness.ipynb, LeveragePoints.ipynb, LeveragePoints.html
Equivariance / Breakdown Point: Equivariance_BP.pdf
Depth / High Breakdown points: Depth.pdf
Location and scale : LocationScale.pdf, Huber_display.ipynb, Huber_display.html
L-estimates and influence functions : L-Estimates.pdf
Gradient descent : GradientDescent.pdf, Gradient_descent.ipynb, Gradient_descent.html
Majorization Minimization : MajorizationMinimization.pdf; MajorizationMinimization.ipynb, MajorizationMinimization.html
Conjugacy : Conjugacy.pdf Conjugate.ipynb, Conjugate.html
Smoothing : Smoothing.pdf Smoothing.ipynb, Smoothing.html
Linear Models :LinearModels.pdf
Quantile regression : QuantileRegression.pdf, QuantileRegression.ipynb, QuantileRegression.html
Robust Optimization : RobustOptim.pdf
Conclusion : Conclusion.pdf