Workshop on High-dimensional Data Analysis

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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)

Confirmed Visitors

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

IMS Auditorium
Detailed instruction on how to get to the institute by public or private transport

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 Stein’s 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

Please complete the online registration form.

Contact

General enquiries: Secretary (ims(AT)nus.edu.sg)