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Effective inference and selection of statistical models to represent latent structure in spatial data

Research Project

Project/Area Number 26330042
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Research Field Statistical science
Research InstitutionOkayama University

Principal Investigator

Sakamoto Wataru  岡山大学, 環境生命科学研究科, 教授 (70304029)

Project Period (FY) 2014-04-01 – 2017-03-31
Project Status Completed (Fiscal Year 2016)
Budget Amount *help
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Keywords階層 Bayes モデル / Markov 確率場 / モデル選択 / 情報量規準 / 領域同定 / 疾病地図データ / APC モデル / 周辺事後分布 / 階層ベイズモデル / マルコフ確率場
Outline of Final Research Achievements

Some methods of making effective inference and selection in statistical models with high-dimensional latent variables are considered to reveal complicated latent structure in spacial and geographical data. It was shown that the method of detecting regions using estimated spatial effect, proposed for application to disease mapping data, had higher possibility of detecting regions with high risk than existing methods. Also it was suggested that the model selection method considered in the analysis of cancer mortality data with age-period-cohort (APC) models could estimate each effect appropriately, and give a new knowledge for interpretation.

Report

(4 results)
  • 2016 Annual Research Report   Final Research Report ( PDF )
  • 2015 Research-status Report
  • 2014 Research-status Report

Research Products

(5 results)

All 2016 2015 2014

All Presentation (5 results) (of which Int'l Joint Research: 4 results,  Invited: 2 results)

  • [Presentation] Cluster detection of disease mapping data based on latent Gaussian Markov random field models2016

    • Author(s)
      Wataru Sakamoto
    • Organizer
      IASC-ARS Conference 2016
    • Place of Presentation
      Daejeon, Korea
    • Year and Date
      2016-11-04
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research / Invited
  • [Presentation] An analysis of Japanese liver cancer mortality data with Bayesian age-period-cohort models2016

    • Author(s)
      Wataru Sakamoto
    • Organizer
      International Conference for JSCS 30th Anniversary
    • Place of Presentation
      Seattle, USA
    • Year and Date
      2016-10-16
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Cluster detection of disease mapping data based on latent Gaussian Markov random field models2016

    • Author(s)
      Wataru Sakamoto
    • Organizer
      COMPSTAT 2016
    • Place of Presentation
      Oviedo, Spain
    • Year and Date
      2016-08-23
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Performance of Bayesian inference with integrated nested Laplace approximation in generalized linear mixed effect models2015

    • Author(s)
      S. Hagihara and W. Sakamoto
    • Organizer
      The 24th South Taiwan Statistical Conference
    • Place of Presentation
      国立彰化師範大学, 彰化市(台湾)
    • Year and Date
      2015-06-28
    • Related Report
      2015 Research-status Report
    • Int'l Joint Research
  • [Presentation] 潜在構造を伴う統計モデルの推測と その複雑さの制御2014

    • Author(s)
      坂本 亘
    • Organizer
      広島大学統計科学研究拠点セミナー
    • Place of Presentation
      広島大学原爆放射線医科学研究所
    • Year and Date
      2014-11-07
    • Related Report
      2014 Research-status Report
    • Invited

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Published: 2014-04-04   Modified: 2018-03-22  

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