Publications
My publications, as in my CV. Also available in BibTeX. My software is hosted on GitHub.
Articles
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Uniform Inference for Kernel Density Estimators with Dyadic Data
M. D. Cattaneo, Y. Feng and W. G. Underwood, 2024
Journal of the American Statistical Association, forthcoming
doi:10.1080/01621459.2023.2272785
arXiv:2201.05967 -
Motif-Based Spectral Clustering of Weighted Directed Networks
W. G. Underwood, A. Elliott and M. Cucuringu, 2020
Applied Network Science 5, 62
doi:10.1007/s41109-020-00293-z
arXiv:2004.01293 -
Simple Poisson PCA: an algorithm for (sparse) feature extraction with simultaneous dimension determination
L. Smallman, W. G. Underwood and A. Artemiou, 2019
Computational Statistics 35, 559-577
doi:10.1007/s00180-019-00903-0
Preprints
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Inference with Mondrian Random Forests
M. D. Cattaneo, J. M. Klusowski and W. G. Underwood, 2023
arXiv:2310.09702 -
Yurinskii’s Coupling for Martingales
M. D. Cattaneo, R. P. Masini and W. G. Underwood, 2022
Annals of Statistics, reject and resubmit
arXiv:2210.00362
Working papers
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Higher-order extensions to the Lindeberg method
M. D. Cattaneo, R. P. Masini and W. G. Underwood -
Adaptive Mondrian Random Forests
M. D. Cattaneo, R. Chandak, J. M. Klusowski and W. G. Underwood
Presentations
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Statistics Seminar, University of Pittsburgh, February 2024
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Statistics Seminar, University of Illinois, January 2024
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Statistics Seminar, University of Michigan, January 2024
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PhD Poster Session, Two Sigma Investments, July 2023
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PhD Research Symposium, Two Sigma Investments, June 2022
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Statistics Laboratory, Princeton University, September 2021
Software
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MondrianForests
Mondrian random forests in Julia
W. G. Underwood, 2023
Available on GitHub -
DyadicKDE
Dyadic kernel density estimation in Julia
W. G. Underwood, 2022
Available on GitHub -
motifcluster
Motif-based spectral clustering of directed networks in R, Python and Julia
W. G. Underwood and A. Elliott, 2020
doi:10.5281/zenodo.3832400
Available on GitHub, CRAN, and PyPI