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Series: Stochastics Seminar

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.)

Series: Stochastics Seminar

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.

Series: Stochastics Seminar

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.

Series: Stochastics Seminar

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.

Series: Stochastics Seminar

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.

Series: Stochastics Seminar

The Potts antiferromagnet on a random graph is a model
problem from disordered systems, statistical mechanics with random
Hamiltonians. Bayati, Gamarnik and Tetali showed that the free energy
exists in the thermodynamic limit, and demonstrated the applicability
of an interpolation method similar to one used by Guerra and
Toninelli, and Franz and Leone for spin glasses. With Contucci,
Dommers and Giardina, we applied interpolation to find one-sided
bounds for the free energy using the physicists' ``replica symmetric
ansatz.'' We also showed that for sufficiently high temperatures, this
ansatz is correct. I will describe these results and some open
questions which may also be susceptible to the interpolation method.

Series: Stochastics Seminar

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 associated random RSK Young tableaux are investigated, when n and m converge
together to infinity. If m does not grow too fast and if the draws are uniform, then 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 word, towards the Tracy?Widom distribution.

Series: Stochastics Seminar

We consider the problem of combining a (possibly uncountably infinite) set of affine estimators
in non-parametric regression model with heteroscedastic Gaussian noise. Focusing onthe exponentially weighted aggregate, we prove a PAC-Bayesian type inequality that leads tosharp oracle inequalities in discrete but also in continuous settings. The framework is general
enough to cover the combinations of various procedures such as least square regression,kernel ridge regression, shrinking estimators and many other estimators used in the literatureon statistical inverse problems. As a consequence, we show that the proposed aggregate provides
an adaptive estimator in the exact minimax sense without neither discretizing the rangeof tuning parameters nor splitting the set of observations. We also illustrate numerically thegood performance achieved by the exponentially weighted aggregate. (This is a joint work with Arnak Dalalyan.)

Series: Stochastics Seminar

We consider weighted random ball model driven by a Poisson random measure on
\Bbb{R}^d\times \Bbb{R}^+\times \Bbb{R} with
product heavy tailed intensity and we are
interested in the functional describing the contribution of the model in
some configurations of \Bbb{R}^d.
The fluctuations of such functionals are
investigated under different types of scaling and the talk will discuss the
possible limits.
Such models arise in communication network to represent the transmission of
information emitted by stations
distributed according to the Poisson measure.

Series: Stochastics Seminar

Hosted by Christian Houdre and Liang Peng.

In this talk I will discuss random matrices that are
matricial analogs of the well known binomial, Poisson, and negative
binomial
random variables. The common thread is the conditional variance of X
given S = X+X', which is a quadratic polynomial in S and in the
univariate case describes
the family of six Meixner laws that will be described in the talk.
The Laplace transform of a general n by n Meixner matrix ensemble
satisfies a system of PDEs which is explicitly solvable for n = 2. The
solutions lead to a family of six non-trivial 2 by 2 Meixner matrix
ensembles. Constructions for the "elliptic cases" generalize to n by n
matrices.
The talk is based on joint work with Gerard Letac.