STM2016
- Dates
- 20/July/2016 (Wed) - 23/July/2016 (Sat)
- Location
- Institute of Statistical Mathematics (ISM), Tokyo, Japan
- Registration Fee
- No registration cost
- Registration Process
-
Email your intention to attend to stm2016
ism.ac.jp
- Organizers
- Gareth W. Peters (UCL) and Tomoko Matsui (ISM)
- The topics include
-
- Extreme value process and heavy tail
- Dependence and stochastic process
- Extreme risk and catastrophe
- Spatial and temporal environment
- Data analysis and sensing
- Machine learning
- Security
- Speakers include
(敬称略) -
- Matthew Ames
- Nourddine Azzaoui
- Guillaume Bagnarosa
- Jennifer S K Chan
- Jen-Tzung Chien
- Laurent Clavier
- Malcom Egan
- Kenji Fukumizu
- Carmine Galasso
- Norikazu Ikoma
- Andrea Macrina
- Konstatin Markov
- Kazuya Takeda
- Tomoko Matsui
- Kazuhiro Minami
- Daisuke Murakami
- Tor Andre Myrvoll
- Ido Nevat
- Gareth W. Peters
- Francois Septier
- Taiji Suzuki
- Dorota Toczydlowska
- Mario Wüthrich
- Yoshiki Yamagata
- Toshinao Yoshiba
- Anna Zaremba
- Shiyong Zhou
- Jiancang Zhuang
- Objective
-
The analysis of complex and massive data sets which display attributes of spatial and temporal characteristics is a growing field of research. Traditionally the two fields have been treated predominantly via a range of different approaches, depending on the discipline in which the applications are under study. For instance in spatial statistics and geo-statistics there is a long history of spatial modeling via regression models, random fields and parametric approaches. There is also a large literature on spatial extremes and the analysis of such features of spatial temporal data. In machine learning there are new approaches being considered based on semi-parametric and non-parametric modeling paradigms which incorporate Bayesian modeling. Then in the signal processing and engineering communities there have been a range of frequency and spatial methods developed based on filters, parametric modeling, regression - basis expansion models, wavelets regression models, and splines.
There has been a range of recent developments in characterizing multivariate spatial and temporal processes which are either discrete (branching and counting processes) or continuous (heavy tailed processes such as Levy processes) and their sub-families the stable processes and Gaussian processes. In addition the study of such processes in practical applications has advanced significantly and the intention of the workshops is to present some recent developments in specification, estimation in high dimensional and complex structured models formed from such processes and application.
Previous years workshop details are available at:
Springer Briefs Special Issue from STM2014 and CSM2014:
book1: Theoretical Aspects of Spatial-Temporal Modeling (ISBN 978-4-431-55336-6)
book2: Modern Methodology and Applications in Spatial-Temporal Modeling (ISBN 978-4-431-55339-7)
