Seminars and Colloquia by Series

Thursday, January 26, 2012 - 15:05 , Location: Skiles 006 , Jon Hosking , IBM Research Division, T. J. Watson Research Center , Organizer: Liang Peng
L-moments are expectations of certain linear combinations of order statistics. They form the basis of a general theory which covers the summarization and description of theoretical probability distributions, the summarization and description of observed data samples, estimation of parameters and quantiles of probability distributions, and hypothesis tests for probability distributions. L-moments are in analogous to the conventional moments, but are more robust to outliers in the data and enable more secure inferences to be made from small samples about an underlying probability distribution. They can be used for estimation of parametric distributions, and can sometimes yield more efficient parameter estimates than the maximum-likelihood estimates. This talk gives a general summary of L-moment theory and methods, describes some applications ranging from environmental data analysis to financial risk management, and indicates some recent developments on nonparametric quantile estimation, "trimmed" L-moments for very heavy-tailed distributions, and L-moments for multivariate distributions.
Thursday, January 19, 2012 - 15:05 , Location: skyles 006 , Christophe Gomez , Department of Mathematics, Stanford University , Organizer: Karim Lounici
In this talk we will describe the different behaviors of solutions of the random Schrödinger with long-range correlations. While in the case of arandom potential with rapidly decaying correlations nontrivial phenomenaappear on the same scale, different phenomena appear on different scalesfor a random potential with slowly decaying correlations nontrivial .
Thursday, December 8, 2011 - 15:05 , Location: skyles 006 , Javier Rojo , Department of Statistics, Rice University , Organizer: Karim Lounici
We review various classifications of probability distributions based on their tail heaviness. Using a characterization of medium-tailed distributions we propose a test for testing the null hypothesis of medium-tail vs long- or short-tailed distributions. Some operating characteristics of the proposed test are discussed.
Thursday, November 10, 2011 - 15:05 , Location: skyles 006 , Ruodu Wang , School of mathematics, Georgia institute of Technology , Organizer: Karim Lounici
The marginal distribution of identically distributed random variables having a constant sum is called a completely mixable distribution. In this talk, the concept, history and present research of the complete mixability will be introduced. I will discuss its relevance to existing problems in the Frechet class, i.e. problems with known marginal distributions but unknown joint distribution and its applications in quantitative risk management.
Thursday, November 3, 2011 - 15:05 , Location: Skiles 006 , Alexey Shashkin , Moscow State University , Organizer:
Excursion sets of stationary random fields have attracted much attention in recent years.They have been applied to modeling complex geometrical structures in tomography, astro-physics and hydrodynamics. Given a random field and a specified level, it is natural to studygeometrical functionals of excursion sets considered in some bounded observation window.Main examples of such functionals are the volume, the surface area and the Euler charac-teristics. Starting from the classical Rice formula (1945), many results concerning calculationof moments of these geometrical functionals have been proven. There are much less resultsconcerning the asymptotic behavior (as the window size grows to infinity), as random variablesconsidered here depend non-smoothly on the realizations of the random field. In the talk wediscuss several recent achievements in this domain, concentrating on asymptotic normality andfunctional central limit theorems.
Thursday, October 27, 2011 - 15:05 , Location: Skyles 006 , Xi Luo , The Wharton School, Department of Statistics, University of Pennsylvania , , Organizer: Karim Lounici
We consider the problem of estimating the covariance matrix. Factormodels and random effect models have been shown to provide goodapproximations in modeling multivariate observations in many settings. These models motivate us to consider a general framework of covariancestructures, which contains sparse and low rank components. We propose aconvex optimization criterion, and the resulting estimator is shown torecover exactly the rank and support of the low rank and sparsecomponents respectively. The convergence rates are also presented. Tosolve the optimization problem, we propose an iterative algorithm basedon Nesterov's method, and it converges to the optimal with order 1/t2for any finite t iterations. Numerical performance is demonstratedusing simulated data and stock portfolio selection on S&P 100.(This is joint work with T. Tony Cai.)
Thursday, October 20, 2011 - 15:05 , Location: Skiles 006 , Don Richards , Penn State, Department of Statistics , Organizer: Karim Lounici
In work on the Riemann zeta function, it is of interest to evaluate certain integrals involving the characteristic polynomials of N x N unitary matrices and to derive asymptotic expansions of these integrals as N -> \infty. In this talk, I will obtain exact formulas for several of these integrals, and relate these results to conjectures about the distribution of the zeros of the Riemann zeta function on the critical line. I will also explain how these results are related to multivariate statistical analysis and to the hypergeometric functions of Hermitian matrix argument.
Thursday, October 6, 2011 - 15:05 , Location: Skiles 006 , Haiyan Cai , Department of Mathematics and Computer Science, University of Missouri , Organizer: Liang Peng
I will talk briefly some of my recent research on random networks. In the first part of the talk, we will focus on the connectivity of a random network. The network is formed from a set of randomly located points and their connections depend on the distance between the points. It is clear that the probability of connection depends on the density of the points. We will explore some properties of this probability as a function of the point density. In the second part, I will discuss a possible approach in the study correlation structure of a large number of random variables. We will focus mainly on Gaussian distribution and distributions which are "similar" to Gaussian distributions. The idea is to use a single number to quantify the strength of correlation among all the random variables. Such a quantity can be derived from a latent cluster structure within a Markovian random network setting.
Thursday, September 29, 2011 - 15:05 , Location: Skiles 006 , David Goldberg , ISyE, Georgia Tech , Organizer:
In this talk, we resolve several questions related to a certain heavy traffic scaling regime (Halfin-Whitt) for parallel server queues, a family of stochastic models which arise in the analysis of service systems.  In particular, we show that the steady-state queue length scales like $O(\sqrt{n})$, and bound the large deviations behavior of the limiting steady-state queue length.  We prove that our bounds are tight for the case of Poisson arrivals.  We also derive the first non-trivial bounds for the steady-state probability that an arriving customer has to wait for service under this scaling.  Our bounds are of a structural nature, hold for all $n$ and all times $t \geq 0$, and have intuitive closed-form representations as the suprema of certain natural processes.  Our upper and lower bounds also exhibit a certain duality relationship, and exemplify a general methodology which may be useful for analyzing a variety of stochastic models.  The first part of the talk is joint work with David Gamarnik.
Thursday, September 22, 2011 - 15:05 , Location: Skiles 006 , Yuri Bakhtin , School of Mathematics, Georgia institute of Technology , Organizer: Karim Lounici
The Burgers equation is a nonlinear PDE and one of the basic hydrodynamic models. The ergodic theory of the Burgers turbulence began with the work of E, Khanin, Mazel, Sinai (Ann. Math. 2000). In their paper and in subsequent papers by Khanin and his coauthors, the compact case (Burgers on a circle or torus) was studied. In this talk, I will discuss the noncompact case. The main object is optimal paths through clouds of Poissonian points.