Niccolo' Anceschi, Ph.D.
I am a Postdoctoral Associate in Statistics at Duke University in Durham, NC (USA).
My research interests are in Bayesian Methods and Computation, Dimensionality Reduction, High-dimensional Regression, Mixed-measurement Data, Approximate Inference & Scalable Computations.
Current research focuses include methodological advancements with high-impact applications in chemical exposures (e.g. firefighters occupational exposure), ecology (species distribution modeling), and precision medicine (multi-omics data).
Publications
Anceschi N., Fasano A., Durante D. and Zanella G. (2023)
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: a review and new results, Journal of the American Statistical Association, 118 (542), 1451-1469
Anceschi N., Hidalgo J., Plata. C., Bellini T., Maritan A., Suweis S. (2019)
Neutral and niche forces as drivers of species selection, Journal of Theoretical Biology 483, 109969
Submitted Papers
Anceschi N., Ferrari F., Mallick H. and Dunson D. (2024+)
Bayesian Joint Additive Factor Models for Multiview Learning, arXiv:2406.00778
Anceschi N., Fasano A., Franzolini A. and Rebaudo G. (2024+)
Scalable expectation propagation for generalized linear models, arXiv:2407.02128
Poworoznek E.,
Anceschi N., Ferrari F. and Dunson D. (2021)
Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching, arXiv:2107.13783
Anceschi N., Rigon T., Zanella G. & Durante D. (2024+)
Optimal lower bounds for logistic log- likelihoods, arXiv:2410.10309