Research Publications

 Zhang, F., Singh, R., and Stufken, J. (2026). An Extension of the GDS-ARM Algorithm for Factor Screening in Mixed-Level Supersaturated Designs. J. Stat. Theory Pract. 20, article 41. https://doi.org/10.1007/s42519-026-00542-x

Stallrich, J., Singh, R., Vogt-Lowell, K., and Li, F. (2025). Powerful Foldover Designs. Qual. Reliab. Eng. Int. 42 (2), 553 - 564, https://doi.org/10.1002/qre.70105   

Singh, R. and Stufken, J. (2024). Factor selection in supersaturated designs by aggregation over random models, Comput. Stat. Data Anal. 194, 107940, https://doi.org/10.1016/j.csda.2024.107940

 Singh, R. (2024). Pareto-efficient designs for multi- and mixed-level supersaturated designs. Stat. Comput. 34, article 38, https://doi.org/10.1007/s11222-023-10354-9

Singh, R. (2023). Best practices for multi- and mixed-level supersaturated designs. J. Qual. Technol. 56 (2), 113 - 127, https://doi.org/10.1080/00224065.2023.2259022 

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 

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 

Chai, F.S., Singh, R., and Stufken, J. (2021). Connected row-column L-designs for symmetrical parallel line assays with two preparations. J. Stat. Theory Pract. 15, article 89. (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

 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), https://ssca.org.in/media/15_19_1_2021_SA_Ashish_Das_3-level-revision.pdf

Singh, R., Kunert, J., and Stufken, J. (2021). On optimal fMRI designs for correlated errors. J. Statist. Plann. Inference 212, 84 - 96, https://doi.org/10.1016/j.jspi.2020.08.003  

Singh, R. and Stufken, J. (2021). Efficient orthogonal fMRI designs in the presence of drift. Stat. Methods Med. Res. 30, 277 - 285, https://doi.org/10.1177/0962280220953870

 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

 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

 Chai, F.-S., Das, A., Singh, R., and Stufken, J. (2020). Discriminating between superior UE(s2)-optimal supersaturated designs. Statistics and Applications 18 (2), 67 - 74 (special issue in honor of Bikas and Bimal Sinha), https://www.ssca.org.in/media/6_Vol._18_No._2_2020_Stufken.pdf

 Singh R., Das, A., and Horsley, D. (2020). SUE(s2)-optimal supersaturated designs. Statist. Probab. Lett. 158, 108673, https://doi.org/10.1016/j.spl.2019.108673

 Das, A. and Singh, R. (2020). A unified approach to discrete choice experiments. J. Statist. Plann. Inference 205, 193 - 202, https://doi.org/10.1016/j.jspi.2019.07.003

 Chai, Feng-Shun, Singh, R., and Stufken, J. (2019). Nearly Magic Rectangles . J. Combin. Des 27 (9), 562 - 567, https://doi.org/10.1002/jcd.21667

Singh, R. and Mukhopadhyay, S. (2019). Exact Bayesian designs for count time series. Comput. Stat. Data Anal. 134, 157 - 170, https://doi.org/10.1016/j.csda.2018.12.008

Singh, R. (2019). On three-level D-optimal paired choice designs. Statist. Probab. Lett. 145, 127 - 132, https://doi.org/10.1016/j.spl.2018.09.005

Das, A., Horsley, D., and Singh, R. (2018). Pseudo Generalized Youden Designs.  J. Combin. Des 26 (9), 439 - 454, https://doi.org/10.1002/jcd.21594

Singh, R., Das, A., and Chai, F.S. (2019). Optimal Paired Choice Block Designs.  Stat. Sinica 29, 1419 - 1438, https://doi.org/10.5705/ss.202016.0084

Horsley, D. and Singh, R. (2018). New lower bounds for t-coverings. J. Combin. Des 26 (8), 369 - 386, https://doi.org/10.1002/jcd.21591

Cheng, C.S., Das, A., Singh, R., and Tsai, P.W. (2018). E(s2)- and UE(s2)-Optimal Supersaturated Designs. J. Statist. Plann. Inference 196, 105 - 114, https://doi.org/10.1016/j.jspi.2017.10.012

Chai, F.S., Das, A., and Singh, R. (2018). Optimal two-level choice designs for estimating main and specified two-factor interaction effects. J. Stat. Theory Pract. 12 (1), 82-92, https://doi.org/10.1080/15598608.2017.1329101

Dey, A., Singh, R., and Das, A. (2017). Efficient paired choice designs with fewer choice pairs. Metrika 80 (3), 309 - 317, https://doi.org/10.1007/s00184-016-0605-9

Chai, F.S., Das, A., and Singh, R. (2017). Three-level A- and D-optimal paired choice designs. Statist. Probab. Lett. 122, 211 - 217, https://doi.org/10.1016/j.spl.2016.11.018

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, https://doi.org/10.1093/biomet/asv040

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

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 

Badal, P.S. and Singh, R. Heuristic Algorithms for Tied Kemeny Rank Aggregation (submitted). 

Collins, D. and Singh, R. Subdata Selection for High-Dimensional Big Data (submitted).  

Phillips, B., Singh, R., and Qiao, X. Subdata Selection for Principal Component Analysis (submitted).

Singh, R. and Stufken, J. Model-free tree-based subdata selection (in preparation).

Singh, R. and Badal, P.S. The R Package RankAggSIgFUR for Efficient Kemeny Rank Aggregation  (in preparation).