Seminars and Colloquia by Series

Thursday, May 6, 2010 - 15:00 , Location: Skiles 269 , Erick Herbin , Ecole Centrale Paris , Organizer:
The aim of this joint work with Ely Merzbach is to present a satisfactory definition of the class of set-indexedL\'evy processes including the set-indexed Brownian motion, the spatial Poisson process, spatial compound Poisson processesand some other stable processes and to study their properties. More precisely, the L\'evy processes are indexed by a quite general class $\mathcal{A}$ of closed subsets in a measure space $(\mathcal{T} ,m)$. In the specific case where $\mathcal{T}$ is the $d$-dimensional rectangle$[0 ,1]^d$ and $m$ is the Lebesgue measure, a special kind of this definition was given and studied by Bass and Pyke and by Adler and Feigin. However, in our framework the parameter set is more general and, it will be shown that no group structure is needed in order to define the increment stationarity property for L\'evy processes.
Thursday, April 22, 2010 - 15:00 , Location: Skiles 269 , Jay Rosen , College of Staten Island, CUNY , Organizer:
We  provide a sufficient condition for the continuity of real valued permanental processes. When applied to the subclass of permanental processes which consists of squares of Gaussian processes,   we  obtain the sufficient condition for continuity which is also known to be necessary.    Using  an isomorphism theorem of Eisenbaum and Kaspi which relates Markov local times and permanental processes  we   obtain a general sufficient condition for the joint continuity of  the local times.
Thursday, April 8, 2010 - 15:00 , Location: Skiles 269 , Alexander Bulinski , Lomonosov Moscow State University , Organizer:
We consider various dependence concepts for random fields. Special attention is paid to Gaussian and shot-noise fields. The multivariate central limit theorems (CLT) are proved for the volumes of excursion sets of stationary quasi-associated random fields on $\mathbb{R}^d$. Formulae for the covariance matrix of the limiting distribution are provided. Statistical versions of the CLT are established as well. They employ three different estimators of the asymptotic covariance matrix. Some numerical results are also discussed.
Thursday, April 1, 2010 - 15:00 , Location: Skiles 269 , Hira Koul , Michigan State University , Organizer: Liang Peng
In this talk we shall discuss the problem of fitting a distribution function to the marginal distribution of a long memory process. It is observed that unlike in the i.i.d. set up, classical tests based on empirical process are relatively easy to implement. More importantly, we discuss fitting the marginal distribution of the error process in location, scale and linear regression models. An interesting observation is that the first order difference between the residual empirical process and the null model can not be used to asymptotically to distinguish between the two marginal distributions that differ only in their means. This finding is in sharp contrast to a recent claim of Chan and Ling to appear in the Ann. Statist. that such a process has a Gaussian weak limit. We shall also proposes some tests based on the second order difference in this case and analyze some of their properties. Another interesting finding is that residual empirical process tests in the scale problem are robust against not knowing the scale parameter. The third finding is that in linear regression models with a non-zero intercept parameter the first order difference between the empirical d.f. of residuals and the null d.f. can not be used to fit an error d.f. This talk is based on ongoing joint work with Donatas Surgailis.
Tuesday, March 30, 2010 - 16:00 , Location: Skiles 269 , Boris Rozovsky , Division of Applied Mathematics, Brown University , Organizer:
We consider a stochastic Navier-Stokes equation driven by a space-time Wiener process. This equation is quantized by transformation of the nonlinear term to the Wick product form. An interesting feature of this type of perturbation is that it preserves the mean dynamics: the expectation of the solution of the quantized Navier-Stokes equation solves the underlying deterministic Navier-Stokes equation. From the stand point of a statistician it means that the perturbed model is an unbiased random perturbation of the deterministic Navier-Stokes equation.The quantized equation is solved in the space of generalized stochastic processes using the Cameron-Martin version of the Wiener chaos expansion. A solution of the quantized version is unique if and only if the uniqueness property holds for the underlying deterministic Navier-Stokes equation. The generalized solution is obtained as an inverse of solutions to corresponding quantized equations. We will also demonstrate that it could be approximated by real (non-generalized processes). A solution of the quantized Navier-Stokes equation turns out to be nonanticipating and Markov. The talk is based on a joint work with R. Mikulevicius.
Thursday, March 11, 2010 - 15:00 , Location: Skiles 269 , Evarist Giné , University of Connecticut , Organizer:
 The almost sure rate of convergence in the sup norm  for  linear wavelet density estimators is obtained, as well as a central limit theorem for the distribution functions based on these estimators. These results are then applied to show that the hard thresholding wavelet estimator of Donoho, Johnstone, Kerkyacharian and Picard (1995) is adaptive in sup norm to the smoothness of a density. An alternative adaptive estimator combining Lepski's method with  Rademacher complexities will also be described. This is joint work with Richard Nickl.  
Thursday, March 4, 2010 - 15:00 , Location: Skiles 269 , Dr Juri Lember , Tartu University, Estonia , , Organizer: Heinrich Matzinger
Abstract: We consider the hidden Markov model, where the dynamic of theprocess is modelled by a  latent Markov chain Y and the observations X aresuch that: 1) given the realization of Y, the observations areindependent; 2) the distribution of the i-th observations (X_i) depends onthe i-th element of the Y (Y_i), only.The segmentation problem consists of estimating the underlying realization(path) of Y given the n observation. Usually the realization with maximumlikelihood, the so called Viterbi alignment is used. On the other hand, itis easy to see that the Viterbi alignment does not minimize the expectednumber of misclassification errors.We consider the segmentation problem in the framework of statisticallearning. This unified risk-based approach helps to analyse many existingalignments as well as defining many new ones. We also study theasymptotics of the risks and infinite alignments.
Thursday, February 25, 2010 - 15:00 , Location: Skiles 269 , Stanislav Molchanov , UNC Charlotte , Organizer:
The talk will present several limit theorems for the supercritical colony of the particles with masses. Reaction-diffusion equations responsible for the spatial distribution of the species contain the usual random death, birth and migration processes. The evolution of the mass of the individual particle includes (together with the diffusion) the mitosis: the splitting of the mass between the two offspring.  The last process leads to the new effects. The limit theorems give the detailed picture of the space –mass distribution of the particles in the bulk of the moving front of the population.
Thursday, January 28, 2010 - 15:00 , Location: Skiles 269 , Stefan Boettcher , Emory Physics , Organizer:
Finding ground states of spin glasses, a model of disordered materials, has a deep connection to many hard combinatorial optimization problems, such as satisfiability, maxcut, graph-bipartitioning, and coloring. Much insight has been gained for the combinatorial problems from the intuitive approaches developed in physics (such as replica theory and the cavity method), some of which have been proven rigorously recently. I present  a treasure trove of numerical data obtained with heuristic methods that suggest a number conjectures, such as an equivalence between maxcut and bipartitioning for r-regular graphs, a simple relation for their optimal configurations as a function of degree r, and anomalous extreme-value fluctuations in a variety of models, hotly debated in physics currently. For some, such as those related to finite-size effects, not even a physics theory exists, for others theory exists that calls for rigorous methods.
Thursday, November 19, 2009 - 15:00 , Location: Skiles 269 , Lijian Yang , Michigan State University , Organizer:
Recently functional data analysis has received considerable attention in statistics research and a number of successful applications have been reported, but there has been no results on the inference of the global shape of the mean regression curve. In this paper, asymptotically simultaneous confidence band is obtained for the mean trajectory curve based on sparse longitudinal data, using piecewise constant spline estimation. Simulation experiments corroborate the asymptotic theory.