This will be the 3rd annual “International Workshop on Perspectives on High-dimensional Data Analysis”, following the first two highly successful workshops entitled: “International Workshop on Perspectives on High-dimensional Data Analysis” (IWPHD) which took place at the Fields Institute, Toronto, 9–11 June 2011 (http://www.fields.utoronto.ca/programs/scientific/10-11/dataanalysis/) and the “International Workshop on Perspectives on High-dimensional Data Analysis II” which was held at the Centre de Recherches Mathématiques, Montréal, 30 May–1 June, 2012 (http://www.crm.umontreal.ca/2012/Perspectives12/index_e.php/11/dataanaly...).
CRM jointly with the American Mathematical society will be publishing an edited volume for the papers presented in this workshop.
The purpose of this workshop is to stimulate research and to foster the interaction of researchers in the area of high-dimensional data analysis in an informal setting. The workshop will provide a venue for participants to meet the field’s leading researchers in a small group setting in order to maximize the chance of interaction and discussion. The objectives include: 1) highlight and expand the breadth of existing methods in high-dimensional data analysis and their potential for the advancement of both mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodology for different application areas; 3) facilitate collaboration between theoretical and subject-area researchers; and 4) provide the opportunity for highly qualified personnel, including students, to interact with leading researchers from countries around the world.
The rationale for such a workshop is the continued rapid advancement of modern technology that is allowing scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high frequency financial data, functional and longitudinal data, and network data, among others. Simultaneous variable selection and estimation is one of the key statistical problems in analyzing such complex data. This joint variable selection and estimation problem is one of the most actively researched topics in the current statistical literature. There have been many advances on the variable selection problem for linear and generalized linear regression models in the past decades. However, more recently, regularization, or penalized, methods are becoming increasingly popular and many new developments have been established.