Speakers
László Györfi, Budapest University of Technology and Economics, Hungary
Nonparametric and machine learning algorithms for time series prediction
Topics:
- Nonparametric regression estimation
- Machine learning aggregation for prediction and for squared loss.
- Pattern recognition.
- Prediction for 0-1 loss.
Sébastien Gros, ESAT, KU Leuven, Belgium
Introduction to Nonlinear Programming (NLP)
Abstract: In the first part of this talk we will review the major properties NLP: global & local optimality, first and second-order optimality conditions, constraint qualification, geometric interpretation and sensitivity analysis. Convex optimization will be approached in more details in the second part of the talk, reviewing duality, generalized constraints, and some classical forms of convex problems. The last part of the presentation will review the state-of-the-art derivative-based numerical optimization methods: Newton and quasi-Newton methods, convergence properties, globalization techniques, quadratic and successive quadratic programming, interior-point methods. The presentation will attempt to make the connections clear between the theory and its practical consequences.
Gérard Biau, Université Pierre et Marie Curie, France
Random Forests
Abstract: Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Despite growing interest and practical use, there has been little exploration of the statistical properties of random forests, and little is known about the mathematical forces driving the algorithm. In this talk, we propose an in-depth analysis of a random forests model suggested by Breiman in 2004, which is very close to the original algorithm. We show in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.
Gert De Rouck, Katholieke Hogeschool Sint Lieven, Belgium
Belgian Beer culture: the biotechnological art of beer creation
Abstract: Belgian beer is well known nowadays in the whole world. More than 1200 different beers in a wide range of beer styles (beers from Trappist Monks, Abbey beers, high alcohol specialty beers, Lagers, and even spontaneous fermented acidic beers) are available in the international market. During this talk, an overview of the history of the Belgian Beer culture will be presented together with the question: what is Belgian Beer culture? Beer is made with only 4 to 6 ingredients. How can Belgian brewers create all these beer styles and varieties with this limited amount of raw materials? Belgian Beer is of high standard and found its roots in out of the box thinking. Also the Belgian Brewing Technology is of outstanding quality. The strength is again in the introduction of innovative processes whereby beer quality and lowest total costs are essential. To conclude: this small country creates great processes and beers that slowly conquer the world. A passion for taste and joy are essential to immerse yourself in our Beer culture.
Léopold Simar, Institut de statistique, Université Catholique de Louvain (UCL), Belgium
Methodological Advances and Perspectives in Nonparametric Frontier Analysis
Abstract: In production theory, economists are interested to determine a production frontier, the boundary of the production set that is the set of all possible combinations of inputs (factor of production like labor, energy, capital,...) and of outputs (the goods that are produced). This boundary represents the locus of optimal combinations of inputs and outputs. Technically, this is equivalent of estimating the boundary of the support of a density of a multivariate random variable where the sample is the observed set of inputs/outputs of firms. The estimated frontier is then used as benchmark against which each firm can be compared and the distance to the optimal frontier serves as a measure of technical inefficiency. Most of the nonparametric estimators are based on the idea of enveloping the cloud of data: this involves the use of advanced statistical tools, but also optimization tools, implying some numerical burden. Recent methodological advances allow today to make inference about the level of inefficiency of firms in fully nonparametric setups. The paper presents the state of the art in this field, showing the technical difficulties linked to the problem where the bootstrap is the only available tool for making practical inference. A lot of challenges are still to be solved and the paper presents the most recent developments and perspectives to address these challenges. This includes: testing issues, robustness to outliers, explaining inefficiencies by environmental factors, introducing noise in the production process...
Göran Kauermann, Institut für Statistik, Ludwig-Maximilians-Universität München, Germany
Penalized Estimation with Applications in Economics and Finance
Abstract: The central idea of penalized estimation is that a functional relationship in the model is fitted with a high dimensional spline basis. A penalty imposed on the fitted spline coefficients induces on the one hand numerical feasibility and stability, on the other hand the penalty guarantees a smooth, that is differentiable fit. The idea is well established in smooth regression or functional data analysis with Eilers & Marx (1996, Statistical Science) marking a milestone. The concept of penalized estimation is however quite general and allows to be employed in various other areas of smooth, that is functional estimation. In the talk we give some examples of the flexibility of the idea, in particular we will establish smooth copula estimation.
The penalty approach itself mirrors close connections to Bayesian estimation, by comprehending the penalty as prior distribution imposed on the spline coefficients. We develop the idea further towards mixture models.
Christophe Croux, Faculty of Business and Economics, Katholieke Universitieit Leuven, Belgium
Robust exponential smoothing
Abstract: Robust versions of the exponential and Holt-Winters smoothing method for forecasting are presented. They are suitable for forecasting univariate time series in the presence of outliers. The robust exponential and Holt-Winters smoothing methods are presented as recursive updating schemes that apply the standard technique to pre-cleaned data. Both the update equation and the selection of the smoothing parameters are robustified. A simulation study compares the robust and classical forecasts. The presented method is found to have good forecast performance for time series with and without outliers, as well as for fat tailed time series and under model misspecification. The method is illustrated using real data incorporating trend and seasonal effects. We also discuss a multivariate extension. This is joint work with Sarah Gelper and Roland Fried.