Modeling Moscow Institute of Physics and Technology

# Conference

Workshop on Modern Statistics and Optimization

# Описание мероприятия

**The workshop will be arranged at the Institute for Information Transmission Problems RAS, Moscow.**

**Room 615**

**February 23-24**

**Organizers: V. Konakov, E. Mammen, V. Spokoiny**

**Local organizers: M. Chuyashkin, Yu. Dorn**

**Registration link** http://goo.gl/forms/3DhSkS4Usd

Full program will be anounced later.

Two mini-course will be given at the Workshop.

**Programm:**

* Alexey Naumov *(The Chinese University of Hong Kong, The Institute for Information Transmission Problems RAS and Lomonosov Moscow State University)

Mini-course:** Recent developments in Random Matrix Theory**

Since the pioneering work of E. Wigner published in 1955 Random Matrix Theory (RMT) became one the most popular mathematical disciplines.

Within the space of 60 years RMT has found its numerous applications in different areas of pure and applied mathematics, physics, biology and financial theory. In my short course I will be mostly interested in Wigner’s semicircle law in macroscopic and microscopic regimes and universality of local eigenvalues statistics. I will give an overview of some classical results and discuss recent developments appeared in the last 10 years. My talk will be partially based on joint results with F. Götze and A. Tikhomirov.

* Marc Hoffmann* (CREST)

Mini-course: **Statistical analysis of transport-fragmentation models**

Дата | Дополнительная информация |
---|---|

24.02.2016 |
9:45 opening 10:00 - 11:30
11:30 - 12:00 break
12:00 - 13:00 Plenary lecture by
13:00 - 13:30
lunch
13:30 - 14:30
14:30 - 16:00
16:00 - 16:30 break
16:30 - 17:00
17:00 - 17:30
In this talk I discuss the problem of constructing minimax lower bounds for the Sobolev balls corresponding to orthonormal systems of Laguerre and Hermite types.
17:30 - 18:00
For specific models such as the time varying autoregressive process it is possible to describe the evolution of the entire process with a finite set of parameter curves. We assume that the locally stationary time series model is known, but the parameter curves are not. For estimation of the curves we use nonparametric kernel-type maximum likelihood estimates which depend on a smoothing parameter (bandwidth). To the best of our knowledge the theoretical behaviour of data adaptive bandwidth choice methods for such estimates has not been considered in the literature. We propose an adaptive bandwidth choice via cross validation, and show that it is asymptotically optimal in a specific sense with respect to a Kullback-Leibler-type distance measure.
24.02.2016
10:00 - 11:30 11:30 - 12:00 break
12:00 – 12:30
The main results describe the accuracy of Gaussian approximation of the posterior distribution in terms of the so called effective dimension. We also show the applicability of these results to density estimation and consider estimation of non-linear functionals of density.
12:30 - 13:00
In the second part of the talk we generalize the rezults mentioned above to sum type and max type functions with implicit affine-sparse structure.
13:00 - 14:00 Launch
14:00 - 15:30
15:30 - 16:00 break
16:00 - 16:30
16:30 - 17:00
17:00 - 17:30
17:30 - 18:00
Abstract: In the framework of statistical learning with square loss, we consider a penalty based on the offset Rademacher process. We prove oracle inequality for this model selection procedure, which provides us with fast rates of convergence. |