| Pannu, J. and Billor, N. Robust sparse functional regression model, Communications in Statistics: Simulations and Computation, 2020 DOI:10.1080/03610918.2015.1096375 . |
| Billor, N. and Turkmen, A.S. Emerging Statistical Methodologies in Twenty- First Century, Invited paper to Women in Industrial and Systems Engineering: Key Advances and Perspectives on Emerging Topics, Springer, 2019, https://www.springer. com/gp/book/9783030118655. |
| Turkmen, A.S., Yuan, Y. and Billor N. Evaluation of Methods for Adjusting Population Strati cation in Genome-wide Association Studies: Standard versus Categorical Principal Component Analysis, Annals of Human Genetics, 2019, https: //www.ncbi.nlm.nih.gov/pubmed/31322288. |
| Lima, I. R., Cao, G. and Billor, N. M-Based Simultaneous Inference for the Mean Function of Functional Data," Annalsf the Institute of Statistical Mathematics, https://link.springer.com/article/10.1007/s10463-018-0656-y, 2019. |
| Pannu, J. and Billor, N. Robust Group-Lasso for Functional Regression Model," Communications in Statistics: Simulations and Computation, 2015 DOI:10.1080/03610918.2015.1096375 . |
| Magnotti, J. and Billor, N. Finding multivariate outliers in fMRI time-series data," Computers and Biology in Medicine, 53, 115 - 124. (Honorable Mention Paper, Top 10% ), 2014. |
| Denhere, M. and Billor, N. Robust Principal Component Functional Logistic Regression," Communications in Statistics: Simulations and Computation, 2013, DOI: 10.1080/03610918.2013.861628. |
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