科研費シンポジウム
"Recent Progress in Spatial and/or Spatio-temporal Data Analysis"
Keynote speaker: Prof. Daniel A. Griffith (Univ. of Texas at Dallas)
2020年10月30日(金)10:00a.m.-オンライン開催
Spatial and/or space- time problems in a wide range of areas have attracted more and more attention especially after 2000, with a proliferation of books, conferences, and papers. The explosion of interests in spatial statistics has been largely fueled by the increased availability of large spatial and spatio-temporal datasets across many fields, such as from the recent progress of geographic information systems (GIS). Spatial statistics can be regarded as one of the most critical areas in statistics to work for many modern issues in the age of big data. This conference welcomes presentations in the broad areas aiming to develop spatial and/or spatio-temporal analysis.
日時： 2020年10月30日(金)10:00－
10:00-11:00 Griffith, D. (Univ. Texas at
Dallas)
Important considerations about space-time data: modeling, scrutiny and
ratification
11:00-11:30 Murakami, D. (ISM)
Compositionally-warped additive mixed modeling for large non-Gaussian data:
Application to COVID-19 analysis
11:30-12:00 Shimono, T. (ISM)
Space-time analysis of COVID-19 in Japan using mobile space statistics(R).
Lunch break
13:30-14:00 Yajima, Y. (Tohoku U.)
On estimation of intrinsically stationary random fields.
14:00-14:30 Sugasawa, S. (Univ. Tokyo)
Spatially clustered regression
14:30-15:00 Toda,H. (Nagoya Institute of Technology)
Post-selection Inference for spatio-temporal trajectory segmentation
break
15:15-15:45 Hirano, T. (Kanto Gakuin U,)
A multi-resolution approximation via linear projection for large spatial
datasets
15:45-16:15 Matsui, M. (Nanzan U.)
Testing independence of continuous time stochastic processes
-- toward independence test for random fields --
16:15-16:45 Matsuda, Y. (Tohoku U.)
Space time ARMA model
基盤研究(A)18H03628「空間計量経済学における最重要課題への挑戦と新たな展開」 研究代表者：堤盛人（筑波大学）
基盤研究(A)20H00576「大規模複雑データの理論と方法論の革新的展開」 研究代表者：青嶋誠（筑波大学）
基盤研究(B)17H01701「CARMA確率場モデルの開発と大規模時空間データ分析への応用」 研究代表者：松田安昌（東北大学）
東北大学 サービス・データ科学研究センター
