The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.

Branislav Vidakovic

Abstract: Wavelet shrinkage methods that use complex-valued wavelets provide additional

insights to shrinkage process compared to standardly used real-valued

wavelets. Typically, a location-type statistical model with an additive noise is

posed on the observed wavelet coefficients and the true signal/image part is estimated

as the location parameter. Under such approach the wavelet shrinkage

becomes equivalent to a location estimation in the wavelet domain. The most

popular type of models imposed on the wavelet coefficients are Bayesian. This

Type of Event:

Harry Zhou

Network analysis is becoming one of the most active research areas in statistics. Significant advances have been made recently on developing theories, methodologies and algorithms for analyzing networks. However, there has been little fundamental study on optimal estimation. In this talk, we establish optimal rate of convergence for graphon estimation. For the stochastic block model with $k$ clusters, we show that the optimal rate under the mean squared error is $n^{-1}\log k+k^2/n^2$.

Type of Event:

Linwei Hu

In design of experiments, optimal designs are designs that  can glean the maximal amount of information from a study.   Therefore, an optimal design can reduce the number of ex- perimental units needed and saving the cost of study.   However, the research  in designing optimal experiments has not kept up with the increasingly complicated structure of data and models; especially for correlated data and multi-covariate models, finding optimal designs is very difficult.

Type of Event:

Jie Yang

We consider D-optimal designs with ordered categorical responses and cumulative link models. In addition to theoretically characterizing locally D-optimal designs, we develop efficient algorithms for obtaining both approximate designs and exact designs. For ordinal data and general link functions, we obtain a simplified structure of the Fisher information matrix, and express its determinant as a homogeneous polynomial. For a predetermined set of design points, we derive the necessary and sufficient conditions for an allocation to be locally D-optimal.

Type of Event:

Sandra Esi Safo

Advancement in technology and computing power have led to the generation of data with enormous amount of variables when compared to the number of observations. These types of data, also known as high dimension, low sample size, are plagued with different challenges that either require modifications of existing traditional methods or development of new statistical methods. One of these challenges is the development of Sparse methods that use only a fraction of the variables.

Type of Event:

Emmanuel Tuglo

The analysis of univariate and multivariate longitudinal data (U/MLD) with censored and missing response has inspired considerable interest in the statistical community recently. In the case of MLD, estimating the contemporaneous correlation coefficient is of particular interest to applied researchers.

Type of Event:

Xiaoming Huo

This talk will have two components. In the first part, I will give an overview of NSF, and its resources that are relevant to the statistical science. Some related programs that are beyond the Division of Mathematical Science, such as the Big Data program and the Computational and Data-enabled Science and Engineering program, will be reviewed. Some suggestions regarding how to apply to these programs may be provided.

Type of Event:

Ping-Shou Zhong

Structured covariance matrices characterized by a small number of parameters have been widely used and play an important role in parameter estimation and statistical inference. To assess the adequacy of a specified covariance structure, one often adopts the classical likelihood-ratio test when the dimension of the data (p) is smaller than the sample size (n). However, this assessment becomes quite challenging when p is bigger than n, since the classical likelihood-ratio test is no longer applicable.

Type of Event: