EPFL Statistics Seminar

  • Prof. Mathieu Ribatet

    Université de Montpellier 2
    January 13, 2012
    15.15 - MA30

    Conditional Simulation of Brown-Resnick processes

    Abstract

    Since many environmental processes such as heat waves or precipitation are spatial in extent, it is likely that a single extreme event affects several locations and the areal modelling of extremes is therefore essential if the spatial dependence of extremes has to be appropriately taken into account. Although some progress has been made to develop a geostatistic of extremes, conditional simulation of max-stable processes is still in its early stage. This paper proposes a framework to get conditional simulations of Brown-Resnick processes. Although closed forms for the regular conditional distribution of Brown-Resnick processes were recently found, sampling from this conditional distribution is a considerable challenge as it leads quickly to a combinatorial explosion. To bypass this computational burden, a Markov chain Monte-Carlo algorithm is presented. We test the method on simulated data and give an application to extreme rainfall around Zurich. Results show that the proposed framework provides accurate conditional simulations of Brown-Resnick processes and can handle real-sized problems.

  • Mr. Kayvan Sadeghi

    University of Oxford
    January 27, 2012
    15.15 - MA3

    Markov Equivalences for Mixed Graphs

    Abstract

    In this talk we describe a class of graphs with three types of edge, called loopless mixed graphs (LMGs). The class of LMGs contains almost all known classes of graphs used in the literature of graphical Markov models as its subclasses. All these graphs use the same interpretation of independence structure called $m$-separation. We motivate the use of LMGs and discuss a number of problems regarding Markov equivalences for LMGs.

  • Prof. Bin Yu [Joint Statistics/IC Seminar]

    University of California at Berkeley
    March 19, 2012
    15.15 - CM1105

    Sparse Modeling: Unified Theory and Movie Reconstruction based on Brain Signal

    Abstract

    Information technology has enabled the collection of massive amounts of data in science, engineering, social science, finance and beyond. Statistics is the science of data and indispensable for extracting useful information from high-dimensional data. After broad successes of statistical machine learning on prediction through regularization, interpretability is gaining attention and sparsity is used as its proxy. With the virtues of both regularization and sparsity, L1 penalized Least Squares(e.g. Lasso) has been intensively studied by researchers from statistics, applied mathematics and signal processing. Lasso is a special case of sparse modeling and has also been the focus of compressive sensing lately. In this talk, I would like to give an overview of both theory and pratcice of Lasso and its extensions. First, I will review theoretical results of Lasso and present an insightful unified M- estimation theory with decomposable penalties under sparse high dimensional statistical models. Second, I will present collaborative research with the Gallant Neuroscience Lab at Berkeley on human understanding visual pathway. In particular, I will show how sparsity modeling enters our movie reconstruction work (dubbed by the TIME Magazine as "mind-reading computers" as one of its 50 Best Inventions of 2011).

  • Prof. Sofia Olhede

    University College London
    April 27, 2012
    15.15 - MA30

    Fourier domain estimation of Lithospheric thickness and moderate sample effects for inference of random fields in the Fourier domain

    Abstract

    The inference for multivariate spatial random fields can either be approached in the spatial domain or in the Fourier domain. In the latter case, asymptotics have been studied and developed for the univariate case by Guyon (1982), Stein (1995), Fuentes (2007) and Robinson (2006). In Geophysics the model for the data is often stated in the Fourier domain, leaving no choice to the analyst in terms of the domain of study, and in addition Fourier domain estimation is usually implemented faster from a numerical perspective than spatial methods. If the spatial sampling is by no means perfect, strategies to ameliorate the moderate sampling effects must be developed. We shall study this problem for the special case of estimating lithospheric flexural rigidity from the set of observations of the Earth's topography and gravity. Such estimates are important as the shallow strength of the lithosphere, Earth's outer layer, influences a variety of processes such as earthquakes, post-glacial rebound, and the like. Traditionally this estimation problem is approached by an iterated technique, which may produce ill-posedness and inefficient estimation, as well as a number of logical fallacies depending on what spectral summary is used as the starting point of the estimation. I show how a fully maximum-likelihood method can be developed, and a number of modelling issues that need to be overcome in order for such methods to work. This is joint work with Frederik Simons (Princeton), sponsored by the EPSRC and the NSF.

  • Prof. Carlos Coelho

    Universidade Nova de Lisboa
    May 4, 2012
    15.15 - MA30

    On the distribution of the product of independent Beta random variables - why near-exact distributions?

    Abstract

    A first approach, based on recently obtained asymptotic expansions of ratios of gamma functions, enables the obtention of the distribution of the product of independent and identically distributed random variables in a much manageable form. However, for the general case, this approach leads to a form which although being much manageable and in line with some previous results, suffers from serious problems of precision and convergence, which have been completely overlooked by other authors and which in most cases prevent its practical use. Nevertheless, it is based on these first results that the authors, using the concept of near-exact distribution, are able to obtain highly manageable but extremely accurate approximations for all cases of the distribution of the product of independent beta random variables. These near-exact approximations, given their high manageability, accuracy and proximity to the exact distribution, may in practice be used instead of the exact distribution.

  • Dr. Aurore Delaigle

    University of Melbourne
    May 10, 2012
    15.15 - MA11

    Nonparametric Regression from Group Testing Data

    Abstract

    To reduce cost and increase speed of large screening studies, data are often pooled in groups. In these cases, instead of carrying out a test (say a blood test) on all individuals in the study to see if they if they are infected or not, one only tests the pooled blood of all individuals in each group. We consider this problem when a covariate is also observed, and one is interested in estimating the conditional probability of contamination. We show how to estimate this conditional probability using a simple nonparametric estimator. We illustrate the procedure on data from the NHANES study.

  • Prof. Laura Sangalli

    Politecnico di Milano
    May 25, 2012
    15.15 - Room TBA

    Title TBA

    Abstract

    TBA

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