At the Summer School of the Swiss Association of Actuaries, in Lausanne, following the part of Jean-Philippe Boucher (UQAM) on telematic data, I will start talking about pictures this Wednesday. Slides are available online
Ewen Gallic (AMSE) will present a tutorial on satellite pictures, and a simple classification problem, related to Alzeimher detection.
We will try to identify what is on the following pictures, starting with the car
(we will see that the car is indeed identified)
We will also discuss previous pictures from the summer school
Next week, with , I will be at the Summer School of the Swiss Association of Actuaries, in Lausanne, with Jean-Philippe Boucher (UQAM) and Ewen Gallic (AMSE).
I will give an introductionary talk on Monday morning, and the slides are now available
There will be some hands-on applications, on R. I will share some codes in the slides.
This morning, we start the SIdE (Italian Econometric Association) Summer School, on Machine Learning Algorithms for Econometricians. Emmanuel Flachaire will start with a presentation of nonparametric econometric techniques. I will then get back to the geometry of (standard) econometric techniques, to introduce kernels. The first series of slides are online.
I will then spend more time on the (popular) idea of “least squares” and mention other loss functions. Slides are online.
After a few years, I decided to put online some lectures notes I had from a graduate course I gave over one (long) day in 2014, in Leuven, entitled “an introduction to multivariate and dynamic risk measures”. The notes are now available on hal. I just hope that it might be usefull to someone…
This week I will be giving a lecture at the 2018 edition of the Summer School at the UB School of Economics, in Barcelona. It will be a four day crash course, starting on Tuesday (morning).
Lecture 1: Introduction : Why Big Data brings New Questions
Lecture 2: Simulation Based Techniques & Bootstrap
Lecture 3: Loss Functions : from OLS to Quantile Regression
Lecture 4: Nonlinearities and Discontinuities
Lecture 5: Cross-Validation and Out-of-Sample diagnosis
Lecture 6: Variable and model selection
Lecture 7: New Tools for Classification Problems
Lecture 8: New Tools for Time Series & Forecasting
Some slides are available on github, and probably more interesting, I will upload a R markdown with all the codes.
Tomorrow and Friday, I will give a crash course on advanced techniques in econometrics. Slides are now online (with animations). There will be breaks to discuss, and I will attend the doctoral seminar on Thursday.
This Tuesday, I will be giving the second part of the (crash) graduate course on advanced tools for econometrics. It will take place in Rennes, IMAPP room, and I have been told that there will be a visio with Nantes and Angers. Slides for the morning are online, as well as slides for the afternoon.
In the morning, we will talk about variable section and penalization, and in the afternoon, it will be on changing the loss function (quantile regression).
This Monday, I will be giving the first part of the (crash) graduate course on advanced tools for econometrics. It will take place in Rennes, IMAPP room, and I have been told that there will be a visio with Nantes and Angers. Slides for the morning are online, as well as slides for the afternoon.
In the morning, we will talk about smoothing techniques, and in the afternoon, it will be on simulations and bootstrap techniques.
Pour le cours de magistère, suite à la première partie du cours, présentant les méthodes de simulation, faite par Magali Fromont, nous allons travailler sur le boostrap, en partant de l’article de Bradly Efron, Six Questions Raised by the Bootstrap. L’article date un peu (il a été publié en 1990) mais il pose six questions qui permettront de revenir sur le fonctionnement du bootstrap (et de la statistique mathématique).
- why do maximum likelihood estimated distributions tend to be short-tailed ?
- why does the delta method tend to underestimate standard errors ?
- why are cross-validation estimators so variable ?
- what is a correct confidence interval ?
- what is a good nonparametric pivotal quantity ?
- what are computationally efficients ways to bootstrap ?
Je me permets d’en rajouter une septième
- why generate samples of size n, why not try smaller sizes ?
Ce dernier point pose la question du subsampling. Je renvoie aux notes de cours de Charles Geyer qui présente ce concept. Je recommande à tous la lecture de What Teachers Should Know about the Bootstrap. Enfin, pour un ouvrage de référence plus moderne que l’article de Bradley Efron, je ne peux que recommander les chapitres 10 et 11 de Computer Age Statistical Inference.