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Fwd: [d-ssc] SSC- Satellite meeting -- International Workshop on Perspectives on High-dimensional Data Analysis (IWPHDA), June 9th to 11th, 2011-Toronto


Begin forwarded message:

From: Ejaz Ahmed <seahmed@uwindsor.ca>
Date: March 28, 2011 12:10:27 PM PDT
Cc: Mu Zhu <m3zhu@uwaterloo.ca>, Peter Song <pxksong@gmail.com>
Subject: [d-ssc] SSC- Satellite meeting -- International Workshop on Perspectives on High-dimensional Data Analysis (IWPHDA), June 9th to 11th, 2011-Toronto

Please broadcast the following announcement to SSC membership.  Thanks, Ejaz.

SSC- Satellite meeting -- International Workshop on Perspectives on High-dimensional Data Analysis (IWPHDA), June 9th to 11th, 2011-Toronto

June 9 to 11, 2011
International Workshop on Perspectives on High-dimensional Data Analysis
Fields Institute, Toronto

Jointly held as a satellite meeting with the 39th Annual Meeting of the SSC.


Organizing Committee
S. Ejaz Ahmed (chair), University of Windsor
Peter X. K. Song, University of Michigan
Mu Zhu, University of Waterloo

Many modern scientific investigations require the analysis of high dimensional data. Examples include genomic and proteomic data, spatial-temporal data, network data, and many others. Simultaneous variable selection and parameter estimation play a central role in such investigations. There is now an immense literature on variable selection, and penalized regression methods are becoming increasingly popular. Much new development has been published in recent years by leading statistical journals.

The application of linear regression models for high-dimensional data analysis is a challenging task. Regularization techniques, such as adaptive penalized least-squares, have attracted much attention in the literature. Penalized regression is a technique for mitigating difficulties arising from collinearity and high-dimensionality. This approach necessarily incurs an estimation bias, while reducing the variance of the estimator. A tuning parameter is needed to adjust the effect of the penalization so that a desirable balance between model parsimony and goodness-of-fit can be achieved. Different forms of penalty functions have been studied intensively over the last 10-15 years. Examples include the LASSO and its many variants (such as adaptive LASSO, group LASSO, relaxed LASSO, and so on), the SCAD, the Dantzig selector, and the elastic net, to name just a few. More recently, some of these penalization/regularization techniques have been extended to deal with the estimation of large covariance matrices, and the analysis of complex dependence structures such as networks and graphs.

The purpose of this workshop is to stimulate research in an informal setting, and to foster the interaction of researchers in the area of high-dimensional data analysis. It will provide a venue for participants to meet leading researches of this field in an intimate setting, providing maximal chance for interaction and discussion. The objectives include:

(1) to highlight and expand the breadth of existing methods in high-dimensional data analysis and their potential for the advance of both mathematical and statistical sciences;

(2) to identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodology for different application areas;

(3) to facilitate collaboration between theoretical and subject-area researchers; and

(4) to provide opportunities for highly qualified personnel to meet and interact with leading researchers from countries around the world.

The workshop is being jointly held as a satellite meeting ahead of the 39th Annual Meeting of the Statistical Society of Canada.

Confirmed Keynote Speakers as of March 15, 2011

Rudy Beran, University of California-Davis
Xihong Lin, Harvard University

Confirmed Invited Speakers as of March 15, 2011

Pierre Alquier, Université Paris 7
Jiahua Chen, University of British Columbia
Kjell Doksum, University of Wisconsin-Madison
Yang Feng, Columbia University
D. A. S. Fraser, University of Toronto
Xin Gao, York University
Yulia Gel, University of Waterloo
Jiashun Jin, Carnegie Mellon University
Timothy D. Johnson, University of Michigan
Abbas Khalili, McGill University
Samuel Kou, Harvard University
Hua Liang, University of Rochester
Yufeng Liu, North Carolina University
Jinchi Lv, University of Southern California
Bin Nan, University of Michigan
Sunil Rao, U of Miami
Xiaotong Shen, University of Minnesota
Sijian Wang, U of Wisconsin-Madison
Chunming Zhang, University of Wisconsin-Madison
Hao Helen Zhang, North Carolina State University
Hongtu Zhu, University of North Carolina
Ji Zhu, University of Michigan

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