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Workshop on High-Dimensional Data Analysis

(27 – 29 Feb 2008)

 ... Jointly organized with Department of Statistics & Applied Probability

 Organizing Committee

Chair

  • Anthony Kuk (National University of Singapore)

Members

  • Zhidong Bai (National University of Singapore)
  • Arup Bose (Indian Statistical Institute, Kolkata)
  • Sanjay Chaudhuri (National University of Singapore)
  • Albert Lo (Hong Kong University of Science and Technology)
     

 Visitors and Participants

 Overview

With the advent of high throughput technologies and powerful computing facilities, the face of the discipline of statistics has changed drastically. According to the executive summary of the NSF Report on the Future of Statistics (http://www.amstat.org/news/nsf4Aug04.pdf), “among the highest priorities for statistics today is adapting to meet the needs of data sets that are so large and complex that new ideas are required, not only to analyze the data, but also to design the experiments and interpret the experimental results”.

The statistical community has clearly embraced this vision, which sees the Issac Newton Institute for Mathematical Sciences organizing a large scale six-month program on Statistical Theory and Methods for Complex, High-Dimensional Data from January to June, 2008 (http://www.newton.cam.ac.uk/programmes/SCH/ws.html).

It is not our intention to duplicate what the Issac Newton Institute is doing. Rather, the IMS would like to complement their program by organizing a regional workshop, with participants from China, Taiwan, India and Singapore, and with the major aim of promoting regional networking and collaboration. As it would be impossible to conduct a comprehensive program over three days, the organizing committee has decided to focus on several niche areas with well represented local expertise. The following three sub-themes are identified:

Day 1: Large dimensional random matrices.

Day 2: Functional data analysis.

Day 3: Sparsity issues and model selection in high dimensional problems.

It is hoped that this timely workshop will lead to fruitful synergy and collaboration between the participants and stimulate further advance in the important and challenging problem of high-dimensional data analysis.

 Venue

 Schedule


Wednesday, 27 Feb 2008

09:30am - 09:45am

Registration

09:45am - 10:00am

Opening Remarks
Louis Chen, Institute for Mathematical Sciences
Anthony Kuk, National University of Singapore

10:00am - 10:45am

Examples of large data analysis
Zhidong Bai, National University of Singapore

10:45am - 11:15am

--- Coffee Break ---

11:15am - 12:00nn

A random-matrices framework for Nyström method
Chii-Ruey Hwang, Institute of Mathematics, Academia Sinica, Taiwan

12:00nn - 01:30pm

--- Lunch Break ---

01:30pm - 02:15pm

Spectra of large dimensional random matrices (LDRM)
Arup Bose, Indian Statistical Institute, India

02:15pm - 03:00pm

Central limit theorem for linear spectral statistics of large dimensional F matrix
Shurong Zheng, Northeast Normal University, China

03:00pm - 03:30pm

--- Coffee Break ---

03:30pm - 04:15pm

Gaussian fluctuations for random matrices
Zhonggen Su, Zhejiang University, China

 

End of Day 1

Thursday, 28 Feb 2008

09:45am - 10:00am

Registration

10:00am - 10:45am

RKHS formulations of some functional data analysis problems
Tailen Hsing, University of Michigan, USA

10:45am - 11:15am

--- Coffee Break ---

11:15am - 12:00nn

Two-Sample test for equal mean functions for curve data
Zhang Jin-Ting, National University of Singapore

12:00nn - 01:30pm

--- Lunch Break ---

01:30pm - 02:15pm

Clustering curves via subspace projection
Jeng-Min Chiou, Institute of Statistical Science, Academia Sinica, Taiwan

02:15pm - 03:00pm

Functional mixture regression
Thomas Lee, The Chinese University of Hong Kong

03:00pm - 03:30pm

--- Coffee Break ---

 

End of Day 2

Friday, 29 Feb 2008

09:00am - 09:15am

Registration

09:15am - 10:00am

Supervised singular value decomposition and its application to independent component analysis for fMRI
Young Truong, The University of North Carolina, USA

10:00am - 10:45am

Model selection, dimension reduction and liquid association: a trilogy via Steinís lemma
Ker-Chau Li,
Institute of Statistical Science, Academia Sinica, Taiwan
University of California, Los Angeles, USA

10:45am - 11:15am

--- Coffee Break ---

11:15am - 12:00nn

Nonlinear dimension reduction with kernel methods
Su-Yun Huang, Institute of Statistical Science, Academia Sinica, Taiwan

12:00nn - 01:30pm

--- Lunch Break ---

01:30pm - 02:15pm

A binary response transformation-expectation estimation in dimension reduction
Lixing Zhu, The Hong Kong Baptist University, Hong Kong

02:15pm - 03:00pm

Sliced regression for dimension reduction
Hansheng Wang, Peking University, China

03:00pm - 03:30pm

--- Coffee Break ---

03:30pm - 04:15pm

Variable selection and coefficient estimation via regularized rank regression
Chenlei Leng, National University of Singapore

04:15pm - 05:00pm

Dimension reduction for unsupervised and partially supervised learning
Debasis Sengupta, Indian Statistical Institute, India

 

End of Day 3


 Registration

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