Debangan Dey
  • Bio
  • Papers
  • Talks
  • Experience
  • Projects
  • Experience
  • Projects
    • External Project
    • Internal Project
  • Conference Talks
    • Graphical Gaussian Process Models for Highly Multivariate Spatial Data
    • Using Mobile Technologies to Investigate Impaired Sleep, Mood, and Energy as Real-Time Triggers of Migraine
    • Modelling of mixed type intensive longitudinal data via Semiparametric Gaussian Copula and its application to real-time mobile monitoring of daily health behaviours
    • Enriching event data - A semi-supervised augmentation approach using location information
    • Semiparametric Gaussian Copula Regression Modeling of Mixed Data Types (SGCRM)
    • Connecting population-level AUC and latent scale-invariant R-square via Semiparametric Gaussian Copula and rank correlations
    • Joint modeling of binary and continuous measurements in large health surveys and its application to network analysis, frailty, and mortality in NHANES 1999-2010
  • Posts
  • Publications
    • Association Between Electronic Diary--Rated Sleep, Mood, Energy, and Stress With Incident Headache in a Community-Based Sample
    • Covariance Estimation and Principal Component Analysis for Mixed-Type Functional Data with application to mHealth in Mood Disorders
    • Integrative Modeling of Accelerometry-Derived Sleep, Physical Activity, and Circadian Rhythm Domains With Current or Remitted Major Depression
    • Modeling Multivariate Spatial Dependencies Using Graphical Models
    • Objectively assessed sleep and physical activity in depression subtypes and its mediating role in their association with cardiovascular risk factors
    • Specificity of affective dynamics of bipolar and major depressive disorder
    • Graph-constrained Analysis for Multivariate Functional Data
    • Graphical Gaussian process models for highly multivariate spatial data
    • Semiparametric gaussian copula regression modeling for mixed data types (sgcrm)
    • Topics in Modeling of Multivariate Mixed Data Types and Highly Multivariate Spatial Data
    • # MeToo and Google inquiries into sexual violence: A hashtag campaign can sustain information seeking
    • Discussion of “An epidemiological forecast model and software assessing interventions on the COVID-19 epidemic in China”
    • Connecting population-level AUC and latent scale-invariant $ R^ 2$ via Semiparametric Gaussian Copula and rank correlations
    • Spatial modeling of shot conversion in soccer to single out goalscoring ability
    • Total volume of physical activity: TAC, TLAC or TAC ($łambda$)
    • ASSOCIATION OF CIRCADIAN REST AND ACTIVITY RHYTHMS WITH BRAIN VOLUMES IN COGNITIVELY NORMAL OLDER ADULTS
    • Re-evaluating the effect of age on physical activity over the lifespan
    • TOTAL PHYSICAL ACTIVITY AND ITS CIRCADIAN ALLOCATION ARE INDEPENDENT PREDICTORS OF MORTALITY
  • Teaching
    • Learn JavaScript
    • Learn Python

Graphical Gaussian process models for highly multivariate spatial data

Jan 1, 2022·
Debangan Dey
,
Abhirup Datta
,
Sudipto Banerjee
· 1 min read
Cite
Type
Journal article
Publication
Biometrika

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Last updated on Jul 8, 2024
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