Publications

Pre-prints

Chi, J. T., & Needell, D. (2023+). Randomized Kaczmarz Linear Discriminant Analysis. arXiv:2211.05749 [stat.CO].
```
@misc{chineedell2022,
  author = {Jocelyn T. Chi and Deanna Needell},
  title = {Randomized Kaczmarz Linear Discriminant Analysis},
  group = {preprint},
  year = {2023+},
  arxiv = {https://arxiv.org/abs/2211.05749},
  howpublished = {arXiv:2211.05749 [stat.CO]},
  pdf = {https://arxiv.org/pdf/2211.05749.pdf}
}
```

Peer-Reviewed Publications

Chi, J. T., & Ipsen, I. C. F. (2022). A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression. Information and Inference, 11(3), 1055–1077. https://doi.org/10.1093/imaiai/iaab016

@article{ChiIpsenRandLS,
  author = {Jocelyn T. Chi and Ilse C. F. Ipsen},
  title = {A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression},
  year = {2022},
  group = {publications},
  arxiv = {https://arxiv.org/abs/1808.05924},
  journal = {Information and Inference},
  volume = {11},
  issue = {3},
  pages = {1055--1077},
  doi = {10.1093/imaiai/iaab016},
  link = {https://academic.oup.com/imaiai/article-abstract/11/3/1055/6347858}
}

Ding, X., Dong, X., McGough, O., Shen, C., Ulichney, A., Xu, R., Swartworth, W., Chi, J. T., & Needell, D. (2022). Population-Based Hierarchical Non-negative Matrix Factorization for Survey Data. Proceedings of the IEEE/ACM International Conference on Big Data Computing, Applications and Technologies: National Symposium for NSF REU Research in Data Science, Systems, and Security, Portland, Oregon, USA, December 6–9, 2022, Accepted.

@inproceedings{phnmf,
  author = {Xiaofu Ding and Xinyu Dong and Olivia McGough and Chenxin Shen and Annie Ulichney and Ruiyao Xu and William Swartworth and Jocelyn T. Chi and Deanna Needell},
  title = {Population-Based Hierarchical Non-negative Matrix Factorization for Survey Data},
  booktitle = {Proceedings of the IEEE/ACM International Conference on Big Data Computing, Applications and Technologies: National Symposium for NSF REU Research in Data Science, Systems, and Security, Portland, Oregon, USA, December 6--9, 2022, Accepted},
  series = {BDCAT 2022},
  year = {2022},
  publisher = {IEEE Computer Society},
  group = {publications},
  arxiv = {https://arxiv.org/abs/2209.04968},
  pdf = {https://arxiv.org/pdf/2209.04968.pdf}
}

Chi, J. T., & Chi, E. C. (2022). A User-Friendly Computational Framework for Robust Structured Regression with the L_2 Criterion. Journal of Computational and Graphical Statistics, 31(4), 1051–1062. https://doi.org/10.1080/10618600.2022.2035232

@article{l2e,
  author = {Jocelyn T. Chi and Eric C. Chi},
  title = {A User-Friendly Computational Framework for Robust Structured Regression with the L$_2$ Criterion},
  year = {2022},
  group = {publications},
  journal = {Journal of Computational and Graphical Statistics},
  code = {https://jocelynchi.github.io/L2E-package-demo/},
  arxiv = {https://arxiv.org/abs/2010.04133},
  pdf = {https://arxiv.org/pdf/2010.04133.pdf},
  doi = {10.1080/10618600.2022.2035232},
  link = {https://www.tandfonline.com/doi/full/10.1080/10618600.2022.2035232},
  volume = {31},
  number = {4},
  pages = {1051-1062},
  publisher = {Taylor & Francis}
}

Chi, J. T., & Ipsen, I. C. F. (2021). Multiplicative Perturbation Bounds for Multivariate Multiple Linear Regression in Schatten p-Norms. Linear Algebra and Its Applications, 624, 87–102. https://doi.org/10.1016/j.laa.2021.03.039

@article{ChiIpsenMMLR,
  author = {Jocelyn T. Chi and Ilse C. F. Ipsen},
  title = {Multiplicative Perturbation Bounds for Multivariate Multiple Linear Regression in {S}chatten $p$-Norms},
  journal = {Linear Algebra and Its Applications},
  year = {2021},
  group = {publications},
  pages = {87-102},
  volume = {624},
  arxiv = {https://arxiv.org/abs/2007.06099},
  doi = {10.1016/j.laa.2021.03.039},
  link = {https://www.sciencedirect.com/science/article/pii/S0024379521001518}
}

Chi, J. T., Ipsen, I. C. F., Hsiao, T.-H., Lin, C.-H., Wang, L.-S., Lee, W.-P., Lu, T.-P., & Tzeng, J.-Y. (2021). SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-based Gene-Environment Interaction Tests in Biobank Data. Frontiers in Genetics, Section Statistical Genetics and Methodology, 12, 1878. https://doi.org/10.3389/fgene.2021.710055

@article{seagle,
  author = {Jocelyn T. Chi and Ilse C. F. Ipsen and Tzu-Hung Hsiao and Ching-Heng Lin and Li-San Wang and Wan-Ping Lee and Tzu-Pin Lu and Jung-Ying Tzeng},
  title = {SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-based Gene-Environment Interaction Tests in Biobank Data},
  year = {2021},
  group = {publications},
  arxiv = {https://arxiv.org/abs/2105.03228},
  journal = {Frontiers in Genetics, section Statistical Genetics and Methodology},
  volume = {12},
  pages = {1878},
  pdf = {https://www.frontiersin.org/articles/10.3389/fgene.2021.710055/pdf},
  code = {http://jocelynchi.com/SEAGLE},
  doi = {10.3389/fgene.2021.710055},
  link = {https://www.frontiersin.org/articles/10.3389/fgene.2021.710055/full}
}

Qin, J., Lee, H., Chi, J. T., Drumetz, L., Chanussot, J., Lou, Y., & Bertozzi, A. L. (2021). Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization. IEEE Transactions on Geoscience and Remote Sensing, 59(4), 3338–3351. https://doi.org/10.1109/TGRS.2020.3020810
```
@article{qinetal2021,
  author = {Jing Qin and Harlin Lee and Jocelyn T. Chi and Lucas Drumetz and Jocelyn Chanussot and Yifei Lou and Andrea L. Bertozzi},
  title = {Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization},
  year = {2021},
  volume = {59},
  issue = {4},
  group = {publications},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  pages = {3338-3351},
  doi = {10.1109/TGRS.2020.3020810},
  pdf = {http://jocelynchi.com/pdf/hyperspectral_final.pdf}
}
```
Qin, J., Lee, H., Chi, J. T., Chanussot, J., Lou, Y., & Bertozzi, A. L. (2019). Fast Blind Hyperspectral Unmixing based on Graph Laplacian. 2019 IEEE Workshop on Hyperspectral Imaging and Signal Processing (WHISPERS).
```
@article{whispers2019,
  author = {Jing Qin and Harlin Lee and Jocelyn T. Chi and Jocelyn Chanussot and Yifei Lou and Andrea L. Bertozzi},
  title = {Fast Blind Hyperspectral Unmixing based on Graph Laplacian},
  journal = {2019 IEEE Workshop on Hyperspectral Imaging and Signal Processing (WHISPERS)},
  year = {2019},
  group = {publications},
  pdf = {http://jocelynchi.com/pdf/cam19-46.pdf}
}
```

Chi, J. T., Chi, E. C., & Baraniuk, R. G. (2016). k-POD: A Method for k-Means Clustering of Missing Data. The American Statistician, 70(1), 91–99. https://doi.org/10.1080/00031305.2015.1086685

@article{ChiChiBaraniuk2016,
  author = {Jocelyn T. Chi and Eric C. Chi and Richard G. Baraniuk},
  title = {$k$-POD: A Method for $k$-Means Clustering of Missing Data},
  journal = {The American Statistician},
  year = {2016},
  volume = {70},
  pages = {91-99},
  doi = {10.1080/00031305.2015.1086685},
  issue = {1},
  group = {publications},
  link = {http://www.tandfonline.com/doi/abs/10.1080/00031305.2015.1086685},
  arxiv = {http://arxiv.org/abs/1411.7013},
  code = {http://jocelynchi.com/kpodclustr}
}

Chi, J. T., & Handcock, M. S. (2014). Identifying Sources of Healthcare Underutilization Among California’s Immigrants. Journal of Racial and Ethnic Health Disparities, 1(3), 207–218. https://doi.org/10.1007/s40615-014-0028-0

@article{ChiHandcock2014,
  author = {Jocelyn T. Chi and Mark S. Handcock},
  title = {Identifying Sources of Healthcare Underutilization Among California's Immigrants},
  journal = {Journal of Racial and Ethnic Health Disparities},
  year = {2014},
  volume = {1},
  pages = {207-218},
  doi = {10.1007/s40615-014-0028-0},
  issue = {3},
  group = {publications},
  pdf = {http://link.springer.com/content/pdf/10.1007%2Fs40615-014-0028-0.pdf},
  link = {https://link.springer.com/article/10.1007/s40615-014-0028-0}
}

Unrefereed Articles

Chi, J. T., & Chi, E. C. (2014). Getting to the Bottom of Matrix Completion and Nonnegative Least Squares with the MM Algorithm. StatisticsViews.com.

@misc{ChiChiMMAlgorithm,
  author = {Jocelyn T. Chi and Eric C. Chi},
  title = {Getting to the Bottom of Matrix Completion and Nonnegative Least Squares with the MM Algorithm},
  howpublished = {StatisticsViews.com},
  month = mar,
  year = {2014},
  pdf = {http://jocelynchi.com/pdf/gettingtothebottom_majorization_minimization_R_tutorial.pdf},
  link = {http://www.statisticsviews.com/details/feature/6035321/Getting-to-the-Bottom-of-Matrix-Completion-and-Nonnegative-Least-Squares-with-th.html},
  group = {unrefereed},
  code = {http://jocelynchi.com/gettingtothebottom/software.html}
}

Chi, J. T., & Chi, E. C. (2014). Getting to the Bottom of Regression with Gradient Descent. StatisticsViews.com.

@misc{ChiChiGradientDescent,
  author = {Jocelyn T. Chi and Eric C. Chi},
  title = {Getting to the Bottom of Regression with Gradient Descent},
  howpublished = {StatisticsViews.com},
  month = jan,
  year = {2014},
  pdf = {http://jocelynchi.com/pdf/gettingtothebottom_gradient_descent_R_tutorial.pdf},
  link = {http://www.statisticsviews.com/details/feature/5722691/Getting-to-the-Bottom-of-Regression-with-Gradient-Descent.html},
  group = {unrefereed},
  code = {http://jocelynchi.com/gettingtothebottom/software.html}
}