SCIENTIFIC PROGRAMS AND ACTIVITIES

September 19, 2014

Distinguished Lecture Series in Statistical Science

Jianqing Fan

Frederick L. Moore Professor of Finance
Director of Committee of Statistical Studies
Department of Operation Research and Financial Engineering,Princeton University

Audio & Slides of the Talks

May 3, 2010 - 3:30 p.m.
Vast-dimensionality and sparsity

May 4, 2010 - 3:30 p.m.
ISIS: A vehicle for the universe of sparsity

Room 230, Fields Institute (map to Fields)

May 3, 2010
Vast-dimensionality and sparsity

Technological innovations have revolutionized the process of scientific research and knowledge discovery. The availability of massive data and challenges from frontiers of research and development have reshaped statistical thinking, data analysis and theoretical studies. The challenges of dimensionality arise from diverse fields of sciences and the humanities, ranging from computational biology and health studies to economics and finance. A comprehensive overview will be given on statistical challenges with vast dimensionality. The impact of dimensionality and spurious correlation will be addressed. What makes the high-dimensional problems feasible is the notion of sparsity.
While the dimensionality can be much higher than the sample size, the intrinsic dimensionality is much smaller. A unified framework expoiting sparsity will be outlined. Other related problems with vast-dimensionality are also discussed. The effectiveness of the method will be illustrated on forecasting home price indexes at zip level.

May 4, 2010
ISIS: A vehicle for the universe of sparsity

Vast-dimensionality characterizes many contemporary statistical problems from genomics and genetics to finance and economics. The challenges are tackled via exploiting the sparsity of problems. We outline a unified framework to ultrahigh dimensional variable selection problems: Iterative applications of vast-scale screening followed by moderate-scale variable selection, resulting in a viable procedure called ISIS. The framework is widely applicable to many statistical contexts: from multiple regression, generalized linear models, survival analysis to machine learning and compress sensing.

The fundamental building blocks are marginal variable screening and penalized likelihood methods. How high dimensionality can such methods handle? How large can false positive and negative be with marginal screening methods? What is the role of penalty functions? This talk will provide some fundamental insights into these problems. The focus will be on the sure screening property, false selection size, the model selection consistency and oracle properties. The advantages of using folded-concave over convex penalty will be clearly demonstrated. The methods will be convincingly illustrated by carefully designed simulation studies and the empirical studies on disease classifications and survival analysis using microarray data and eQTL.



The Distinguished Lecture Series in Statistical Science series was established in 2000 and takes place annually. It consists of two lectures by a prominent statistical scientist. The first lecture is intended for a broad mathematical sciences audience. The series occasionally takes place at a member university and is tied to any current thematic program related to statistical science; in the absence of such a program the speaker is chosen independently of current activity at the Institute. A nominating committee of representatives from the member universities solicits nominations from the Canadian statistical community and makes a recommendation to the Fields Scientific Advisory Panel, which is responsible for the selection of speakers.

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