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Creators/Authors contains: "Wood, Ian"

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  1. ; ; ; ; ; ; (, ACM)
    Free, publicly-accessible full text available July 4, 2026
  2. ; ; ; (, Mathematika)
    null (Ed.)
    For the Schrödinger equation −d2u/dx2+q(x)u=λu on a finite x-interval, there is defined an "asymmetry function" a(λ;q), which is entire of order 1/2 and type 1 in λ. Our main result identifies the classes of square-integrable potentials q(x) that possess a common asymmetry function. For any given a(λ), there is one potential for each Dirichlet spectral sequence. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; (, Annual Review of Biomedical Data Science)
    Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use. 
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    Full Text Available 
  4. ; ; ; (, Canadian Journal of Statistics)
    This paper considers the latent Gaussian graphical model, which extends the Gaussian graphical model to handle discrete data as well as mixed data with both continuous and discrete variables by assuming that discrete variables are generated by discretizing latent Gaussian variables. We propose a modified expectation‐maximization (EM) algorithm to estimate parameters in the latent Gaussian model for binary data. We also extend the proposed modified EM algorithm to the latent Gaussian model for mixed data. The conditional dependence structure can be consequently constructed by exploring the sparsity pattern of the precision matrix of the latent variables. We illustrate the performance of our proposed estimator through comprehensive numerical studies and an application to voting data of the United Nations General Assembly. 
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