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Creators/Authors contains: "Yu, Shan"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, ICML)
    Free, publicly-accessible full text available July 6, 2027
  2. ; ; ; ; ; ; ; ; ; ; et al (, Forty-third International Conference on Machine Learning, ICML 2026)
    Free, publicly-accessible full text available June 24, 2027
  3. ; ; (, Journal of Experimental Child Psychology)
    Free, publicly-accessible full text available February 1, 2027
  4. ; ; ; ; ; ; ; ; ; ; et al (, USENIX)
    Free, publicly-accessible full text available July 13, 2027
  5. ; ; ; (, Journal of the American Statistical Association)
    Full Text Available 
  6. ; ; ; (, Journal of Nonparametric Statistics)
    Full Text Available 
  7. ; ; ; (, Journal of Computational and Graphical Statistics)
    Full Text Available 
  8. ; (, Genome Biology)
    Abstract Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC’s accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis. 
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  9. ; ; ; ; (, Journal of Nonparametric Statistics)
    Full Text Available 
  10. ; ; ; (, Journal of computational and graphical statistics)
    Accepted Manuscript Full Text Available