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update bibtex for k-sample #832

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update bibtex for k-sample #832

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28 changes: 14 additions & 14 deletions content/bibs/pubs.bib
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Expand Up @@ -1551,20 +1551,20 @@ @article{shenLearningInterpretableCharacteristic2020
copyright={All rights reserved}
},

@misc{panda2023highdimensional,
title={High-Dimensional and Universally Consistent K-Sample Tests},
author={Panda, Sambit and Shen, Cencheng and Perry, Ronan and Zorn, Jelle and Lutz, Antoine and Priebe, Carey E. and Vogelstein, Joshua T.},
author+an={1=trainee;2=trainee;3=trainee;7=highlight},
year={2023},
month={10},
number={arXiv:1910.08883},
eprint={1910.08883},
primaryclass={cs, stat},
journal={arXiv},
doi={10.48550/arXiv.1910.08883},
archiveprefix={arXiv},
keywords={in-review},
copyright={All rights reserved}
@article{panda2025universally,
title = {Universally Consistent {{K-sample}} Tests via Dependence Measures},
author = {Panda, Sambit and Shen, Cencheng and Perry, Ronan and Zorn, Jelle and Lutz, Antoine and Priebe, Carey E. and Vogelstein, Joshua T.},
author+an={1=trainee;2=trainee;3=trainee;7=highlight},
year = {2025},
month = jan,
journal = {Statistics \& Probability Letters},
volume = {216},
pages = {110278},
issn = {0167-7152},
doi = {10.1016/j.spl.2024.110278},
urldate = {2024-09-24},
abstract = {The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical method to test mean differences, along with several recent methods to test distributional differences. In this paper, we demonstrate the existence of a transformation that allows K-sample testing to be carried out using any dependence measure. Consequently, universally consistent K-sample testing can be achieved using a universally consistent dependence measure, such as distance correlation and the Hilbert--Schmidt independence criterion. This enables a wide range of dependence measures to be easily applied to K-sample testing.},
keywords = {in-review},
},

@article{lein2007genome,
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