## Seminars and Colloquia by Series

Tuesday, November 3, 2009 - 16:00 , Location: Skiles 255 (Note unusual time and location) , Ivan NOURDIN , Paris VI , Organizer:
My aim is to explain how to prove multi-dimensional central limit theorems for the spectral moments (of arbitrary degrees) associated with random matrices with real-valued i.i.d. entries, satisfying some appropriate moment conditions. The techniques I will use rely on a universality principle for the Gaussian Wiener chaos as well as some combinatorial estimates. Unlike other related results in the probabilistic literature, I will not require that the law of the entries has a density with respect to the Lebesgue measure. The talk is based on a joint work with Giovanni Peccati, and use an invariance principle obtained in a joint work with G. P. and Gesine Reinert
Thursday, October 22, 2009 - 15:00 , Location: Skiles 269 , Ton Dieker , (ISyE, Georgia Tech) , Organizer:
In this talk, we study an interacting particle system arising in the context of series Jackson queueing networks. Using effectively nothing more than the Cauchy-Binet identity, which is a standard tool in random-matrix theory, we show that its transition probabilities can be written as a signed sum of non-crossing probabilities. Thus, questions on time-dependent queueing behavior are translated to questions on non-crossing probabilities. To illustrate the use of this connection, we prove that the relaxation time (i.e., the reciprocal of the ’spectral gap’) of a positive recurrent system equals the relaxation time of a single M/M/1 queue with the same arrival and service rates as the network’s bottleneck station. This resolves a 1985 conjecture from Blanc on series Jackson networks. Joint work with Jon Warren, University of Warwick.
Friday, October 9, 2009 - 15:00 , Location: Skiles 154 (Unusual time and room) , Carl Mueller , University of Rochester , Organizer:
One of the most important stochastic partial differential equations, known as the superprocess, arises as a limit in population dynamics. There are several notions of uniqueness, but for many years only weak uniqueness was known.  For a certain range of parameters, Mytnik and Perkins recently proved strong uniqueness.  I will describe joint work with Barlow, Mytnik and Perkins which proves nonuniqueness for the parameters not included in Mytnik and Perkins' result.  This completely settles the question for strong uniqueness, but I will end by giving some problems which are still open.
Thursday, October 1, 2009 - 15:00 , Location: Skiles 269 , Denis Bell , University of North Florida , Organizer:
The Black‐Scholes model for stock price as geometric Brownian motion, and the corresponding European option pricing formula, are standard tools in mathematical finance. In the late seventies, Cox and Ross developed a model for stock price based on a stochastic differential equation with fractional diffusion coefficient. Unlike the Black‐Scholes model, the model of Cox and Ross is not solvable in closed form, hence there is no analogue of the Black‐Scholes formula in this context. In this talk, we discuss a new method, based on Stratonovich integration, which yields explicitly solvable arbitrage‐free models analogous to that of Cox and Ross. This method gives rise to a generalized version of the Black‐Scholes partial differential equation. We study solutions of this equation and a related ordinary differential equation.
Thursday, September 24, 2009 - 15:00 , Location: Skiles 269 , Jim Nolen , Duke University , Organizer:
I will describe recent work on the behavior of solutions to reaction diffusion equations (PDEs) when the coefficients in the equation are random.  The solutions behave like traveling waves moving in a randomly varying environment.  I will explain how one can obtain limit theorems (Law of Large Numbers and CLT) for the motion of the interface.  The talk will be accessible to people without much knowledge of PDE.
Thursday, September 10, 2009 - 15:00 , Location: Skiles 269 , Christian Houdré , Georgia Tech , Organizer:

Given a random word of size n whose letters are drawn independently
from an ordered alphabet of size m, the fluctuations of the shape of
the corresponding random RSK Young tableaux are investigated, when both
n and m converge together to infinity. If m does not grow too fast and
if the draws are uniform, the limiting shape is the same as the
limiting spectrum of the GUE. In the non-uniform case, a control of
both highest probabilities will ensure the convergence of the first row
of the tableau, i.e., of the length of the longest increasing
subsequence of the random word, towards the Tracy-Widom distribution.

Thursday, September 3, 2009 - 15:00 , Location: Skiles 269 , Philippe Rigollet , Princeton University , Organizer:
The goal of this talk is to present a new method for sparse estimation which does not use standard techniques such as $\ell_1$ penalization. First, we introduce a new setup for aggregation which bears strong links with generalized linear models and thus encompasses various response models such as Gaussian regression and binary classification. Second, by combining maximum likelihood estimators using exponential weights we derive a new procedure for sparse estimations which satisfies exact oracle inequalities with the desired remainder term. Even though the procedure is simple, its implementation is not straightforward but it can be approximated using the Metropolis algorithm which results in a stochastic greedy algorithm and performs surprisingly well in a simulated problem of sparse recovery.
Thursday, August 27, 2009 - 15:00 , Location: Skiles 269 , Dabao Zhang , Purdue University , Organizer:
We propose a penalized orthogonal-components regression (POCRE) for large p small n data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlation to the response residuals. A new penalization framework, implemented via empirical Bayes thresholding, is presented to effectively identify sparse predictors of each component. POCRE is computationally efficient owing to its sequential construction of leading sparse principal components. In addition, such construction offers other properties such as grouping highly correlated predictors and allowing for collinear or nearly collinear predictors. With multivariate responses, POCRE can construct common components and thus build up latent-variable models for large p small n data. This is an joint work with Yanzhu Lin and Min Zhang
Thursday, April 23, 2009 - 15:00 , Location: Skiles 269 , Yichuan Zhao , Department of Mathematics, Georgia State University , Organizer: Heinrich Matzinger
It is of interest that researchers study competing risks in which subjects may fail from any one of k causes. Comparing any two competing risks with covariate effects is very important in medical studies. In this talk, we develop omnibus tests for comparing cause-specific hazard rates and cumulative incidence functions at specified covariate levels. The omnibus tests are derived under the additive risk model by a weighted difference of estimates of cumulative cause-specific hazard rates. Simultaneous confidence bands for the difference of two conditional cumulative incidence functions are also constructed. A simulation procedure is used to sample from the null distribution of the test process in which the graphical and numerical techniques are used to detect the significant difference in the risks. In addition, we conduct a simulation study, and the simulation result shows that the proposed procedure has a good finite sample performance. A melanoma data set in clinical trial is used for the purpose of illustration.
Thursday, April 16, 2009 - 15:00 , Location: Skiles 269 , Vladimir I. Koltchinskii , School of Mathematics, Georgia Tech , Organizer: Heinrich Matzinger
In binary classification problems, the goal is to estimate a function g*:S -> {-1,1} minimizing the generalization error (or the risk) L(g):=P{(x,y):y \neq g(x)}, where P is a probability distribution in S x {-1,1}. The distribution P is unknown and estimators \hat g of g* are based on a finite number of independent random couples (X_j,Y_j) sampled from P. It is of interest to have upper bounds on the excess risk {\cal E}(\hat g):=L(\hat g) - L(g_{\ast}) of such estimators that hold with a high probability and that take into account reasonable measures of complexity of classification problems (such as, for instance, VC-dimension). We will discuss several approaches (both old and new) to excess risk bounds in classification, including some recent results on excess risk in so called active learning.