Research Publications

Published/Accepted:

1. Zhang, F., Singh, R., and Stufken, J. (2026). An Extension of the GDS-ARM Algorithm for Factor Screening in Mixed-Level Supersaturated Designs. Journal of Statistical Theory and Practice, to appear.
2. Stallrich, J., Singh, R., Vogt-Lowell, K., & Li, F. Powerful Foldover Designs. (2025) Quality and Reliability Engineering International, accepted, [https://doi.org/10.1002/qre.70105]
3. Singh, R. and Stufken, J. (2024). Factor selection in supersaturated designs by aggregation over random models, Computational Statistics & Data Analysis, accepted. [https://doi.org/10.1016/j.csda.2024.107940]
4. Singh, R. (2024). Pareto-efficient designs for multi- and mixed-level supersaturated designs. Statistics & Computing 34, article 38. [https://doi.org/10.1007/s11222-023-10354-9]
5. Singh, R. (2023). Best practices for multi- and mixed-level supersaturated designs. J. Quality Technology, to appear. [https://doi.org/10.1080/00224065.2023.2259022]
6. Singh, R. and Stufken, J. (2023). Subdata selection with a large number of variables. The New England Journal of Statistics in Data Science, 1(3), 426-438. [https://doi.org/10.51387/23-NEJSDS36]
7. Singh, R. and Stufken, J. (2023). Selection of two-level supersaturated designs for main effects models. Technometrics, 65, 96-104. [https://doi.org/10.1080/00401706.2022.2102080]
8. Chai, F.-S., Singh, R. and Stufken, J. (2021). Connected row-column L-designs for symmetrical parallel line assays with two preparations. Journal of Statistical Theory and Practice, accepted. (Special invited issue on 'State of the art in research on design and analysis of experiments'). [https://doi.org/10.1007/s42519-021-00219-7]
9. Singh, R., Das, A. and Chai, F.-S. (2021). On Three-Level A-Optimal Designs for Test-Control Discrete Choice Experiments. Statistics and Applications 19(1), 199-208 (special issue in honor of Aloke Dey). [Link]
10. Singh, R., Kunert, J. and Stufken, J. (2021). On optimal fMRI designs for correlated errors. Journal of Statistical Planning and Inference 212, 84-96. [https://doi.org/10.1016/j.jspi.2020.08.003]
11. Singh, R. and Stufken, J. (2021). Efficient orthogonal fMRI designs in the presence of drift. Statistical Methods in Medical Research 30, 277-285. [https://doi.org/10.1177/0962280220953870 ]
12. Singh, R. and Kunert, J. (2021). Efficient crossover designs for non-regular settings. Metrika, 84(4), 497-510. [https://doi.org/10.1007/s00184-020-00780-4]
13. Singh, R., Dean, A., Das, A. and Sun, F. (2021). A-optimal designs under a linearized model for discrete choice experiments. Metrika 84(4), 445-465. [https://doi.org/10.1007/s00184-020-00771-5]
14. Chai, F.-S., Das, A., Singh, R. and Stufken, J. (2020). Discriminating between superior UE(s^2)-optimal supersaturated designs. Statistics and Applications 18(2), 67--74 (special issue in honor of Bikas and Bimal Sinha). [Link]
15. Singh R., Das, A. and Horsley, D. (2020). SUE(s2)-optimal supersaturated designs. Statistics & Probability Letters 158, 108673. [PDF] [Supp]
16. Das, A. and Singh R. (2020) A unified approach to discrete choice experiments. Journal of Statistical Planning and Inference 205, 193-202 [PDF] [Supp]
17. Chai, F.-S., Singh, R. and Stufken, J. (2019). Nearly Magic Rectangles. Journal of Combinatorial Designs 27(9), 562-567 [PDF]
18. Singh, R. and Mukhopadhyay, S. (2019). Exact Bayesian designs for count time series, Computational Statistics & Data Analysis 134, 157-170. [PDF]
19. Singh, R. (2019). On 3-level optimal choice designs, Statistics & Probability Letters 145, 127-132. [PDF]
20. Das, A., Horsley, D. and Singh, R. (2018). Pseudo generalized Youden Designs. Journal of Combinatorial Designs 26(9), 439-454 [PDF]
21. Singh, R., Das, A. and Chai, F.-S. (2018). Optimal Paired Choice Block Designs. Statistica Sinica 29(3), 1419-1438 [PDF] [Supp]
22. Horsley, D. and Singh, R. (2018). New lower bounds for t-coverings. Journal of Combinatorial Designs 26(8), 369-386 [PDF]
23. Cheng, C-S, Das, A., Singh R. and Tsai, P-W. (2018). E(s2) - and UE(s2)-Optimal Supersaturated Designs. Journal of Statistical Planning and Inference 196, 105-114 [PDF] [Supp]
24. Chai, F.-S., Das, A. and Singh, R. (2018). Optimal two-level choice designs for estimating main and specified two-factor interaction effects. Journal of Statistical Theory and Practice 12(1), 82-92 [PDF]
25. Dey, A., Singh, R. and Das, A. (2017). Efficient paired choice designs with fewer choice pairs. Metrika 80(3), 309–317 [PDF]
26. Chai, F.-S., Das, A. and Singh, R. (2017). Three-level A- and D-optimal paired choice designs. Statistics & Probability Letters 122, 211--217. [PDF]
27. Singh, R., Chai, F.-S. and Das, A. (2015). Optimal two-level choice designs for any number of choice sets. Biometrika 102(4), 967--973. [PDF] [Supp]

Tools/Packages

1. Parker, H, Singh, R, Badal, PS. “RankAggSIgFUR: Polynomially Bounded Rank Aggregation under Kemeny's Axiomatic Approach.” R Package version 0.1.0 (2022) https://cran.r-project.org/web/packages/RankAggSIgFUR/index.html
2. Singh, R, Stufken, J. “GDSARM: Gauss-Dantzig Selector - Aggregation over Random Models.” R Package version 0.1.0 (2022) https://cran.r-project.org/web/packages/GDSARM/index.html

Under revision/submitted/in preparation:

1. Badal, P.S. and Singh, R. Heuristic Algorithms for Tied Kemeny Rank Aggregation (submitted).
2. Collins, D. and Singh, R. Subdata Selection for High-Dimensional Big Data (submitted).
3. Singh, R. and Stufken, J. Model-free tree-based subdata selection (in preparation).
4. Singh, R. and Badal, P.S. The R Package RankAggSIgFUR for Efficient Kemeny Rank Aggregation (in preparation).

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