Analytics, Information and Operations Management publications


2023 and forthcoming

Arikan, M., Demir, S., & Erkoc, M. (2023). Inventory management with advance supply contracts across multiple replenishment periods. Asia-Pacific Journal of Operational Research, 40 (3): 2250031.

Arikan, M., Kara, M., Masli, A., & Xi, Y. (2023). Political euphoria and corporate disclosures: An investigation of CEO partisan alignment with the president of the United States. Journal of Accounting & Economics, 75 (2–3): 101552.

Banerjee, T., Liu, P., Mukherjee, G., Dutta, S., & Che, H. (2023). Joint modeling of playing time and purchase propensity in massively multiplayer online role-playing games using crossed random effects. Annals of Applied Statistics, 17 (3): 2533–2554.

Chakraborty, S., Ma, A., & Swinney, R. (2023). Designing rewards-based crowdfunding campaigns for strategic (but distracted) contributors. Naval Research Logistics, 70 (1): 3–20.

Gupta, S., Chen, W., Janakiraman, G., & Dawande, M. (2023). 3 years, 2 papers, 1 course off: Optimal non-monetary reward policies. Management Science, 69 (5): 2852–2869.

Garg, A., Demirezen, E. M., Dogan, K., & Cheng, H. K. (in press). Financial sustainability of IoT platforms: The role of quality and security. Production & Operations Management.

Lash, M. T., Sajeesh, S., & Araz, O. M. (2023). Predicting mobility using limited data during early stages of a pandemic. Journal of Business Research, 157: 113413.

Li, S., Schneider, M. J., Yu, Y., & Gupta, S. (2023). Re-identification risk in panel data: Protecting for k-anonymization. Information Systems Research, 34 (3): 1066–1088.

Li, S., Tian, S., Yu, Y., Zhu, X., & Lian, H. (2023). Corporate default probability: A discrete single-index hazard model approach. Journal of Business & Economic Statistics, 41 (4): 1288–1299.

Zhu, X., Li, S., Srinivasan, K., & Lash, M. T. (in press). Impact of the COVID-19 pandemic on the stock market and investor online word of mouth. Decision Support Systems.

Sethuraman, N., Parlaktürk, A. K., & Swaminathan, J. M. (2023). Personal fabrication as an operational strategy: Value of delegating production to customer using 3D printing. Production & Operations Management, 32 (7): 2362–2375. 

Tan, Y., Shenoy, P. P., Sherwood, B., Gaddy, M., Shenoy, C., & Oehlert, M. (in press). Bayesian network models for PTSD screening in veterans. INFORMS Journal on Computing.

Allenbrand, C. & Sherwood, B. (2023). Model selection uncertainty and stability in beta regression models: A study of bootstrap-based model averaging with an empirical application to clickstream data. Annals of Applied Statistics, 17 (1): 680–710.

Maidman, A, Wang, L, Zhou, X-H, & Sherwood, B. (2023). Quantile partially linear additive model for data with dropouts and an application to modeling cognitive decline. Statistics in Medicine, 42 (16): 2729–2745.

Sherwood, B. & Price, B.S. (in press). On the use of minimum penalties in statistical learning. Journal of Computational & Graphical Statistics.

Augusto, F. B., Numfor, E., Srinivasan, K., Iboi, E., Fulk, A., Saint Onge, J. M., & Peterson, T. (2023). Impact of public sentiments on the transmission of COVID-19 across a geographical gradient. PeerJ, 11: e14736.

Jiang, J., & Srinivasan, K. (2023). MoreThanSentiments: A text analysis package. Software Impacts, 15: 100456.

Kim, B., Srinivasan, K., Kong, S. H., Kim, J. H., Shin, C. S., & Ram, S. (2023). ROLEX: A novel method for interpretable machine learning using robust local explanations. MIS Quarterly, 47 (3): 1303–1332.

Chauhan, S. S., Srinivasan, K., & Sharma, T. (2023). A trans-national comparison of stock market movements and related social media chatter during the COVID-19 pandemic. Journal of Business Analytics, 6 (3): 203–316.

Srinivasan, K. (2023). Graph data management, modeling, and mining. In Encyclopedia of Data Science & Machine Learning. IGI Global. Doi: 10.4018/978-1-7998-9220-5.

Srinivasan, K., et al. (2023). Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling. npj Digital Medicine, 6: Article 5. 

Srinivasan, K., Currim, F., & Ram, S. (2023). A human-in-the-loop segmented mixed-effects modeling method for analyzing wearables data. ACM Transactions on Management Information Systems, 14 (2): Article 18.

Srinivasan, K., & Jiang, J. (2023). Examining disease multimorbidity in U.S. hospital visits before and during COVID-19 pandemic: A graph analytics approach, ACM Transactions on Management Information Systems, 14 (2): Article 17.

Deng, J., Ghasemkhani, H., Tan, Y., & Tripathi, A. K. (2023). Actions speak louder than words: Imputing users’ reputation from transaction history. Production & Operations Management, 32 (4): 1096–1111.


Han, Z., Arikan, M., & Mallik, S. (2022). Competition between hospitals under bundled payments and fee-for-service: An equilibrium analysis of insurer’s choice. Manufacturing & Service Operations Management, 24 (3): 1821–1842. 

Bernstein, F., Chakraborty, S., & Swinney, R. (2022). Intertemporal content variation with customer learning. Manufacturing & Service Operations Management, 24 (3):1664–1680.

Bloodgood, J. & Chen, A. N. K. (2022). Preventing organizational knowledge leakage: The influence of knowledge seekers’ awareness, motivation, and capability. Journal of Knowledge Management, 26 (9): 2145–2176.

Dey, D., Ghoshal, A., & Lahiri, A. (2022). Circumventing circumvention: An economic analysis of the role of education and enforcement. Management Science, 68 (4): 2914–2931.

Sherwood, B. & Li, S. (2022). Quantile regression feature selection and estimation with grouped variables using Huber approximation. Statistics and Computing, 32 (5): 1–16.

Reed, S., Campbell, A. M., & Thomas, B. W. (2022). The value of autonomous vehicles for last-mile deliveries in urban environments. Management Science, 68 (1): 280–299.

Reed, S., Campbell, A., & Thomas, B. (2022). Impact of autonomous vehicle assisted last-mile delivery in urban to rural settings. Transportation Science, 56 (6):1530–1548.

Aldrich, J. C., Dawid, A. P., Denœux, T., Shenoy, P. P., & Vovk, V. (2022). Probability and statistics: Foundations and history. International Journal of Approximate Reasoning, 141 (2): 1–4.

Aldrich, J. C., Dawid, A. P., Denœux, T., Shenoy, P. P., & Vovk, V. (2022). Glenn Shafer – a short biography. International Journal of Approximate Reasoning, 141 (2): 5–10.

Jiroušek, R., Kratochvìl, V., & Shenoy, P. P. (2022). Entropy for evaluation of Dempster-Shafer belief function models. International Journal of Approximate Reasoning, 151 (12): 164–181.

Jiroušek, R., Kratochvíl, V., & Shenoy, P. P. (2022). On conditional belief functions in the Dempster-Shafer theory, in S. Le Hégarat-Mascle, I. Bloch, and E. Aldea (eds.), Belief Functions: Theory and Applications, 7th International Conference, BELIEF 2022, Lecture Notes in Artificial Intelligence, Vol. 13506, 207–218, 2022, Springer Cham, Switzerland.

Sherwood, B. & Maidman, A. (2022). Additive nonlinear quantile regression in ultra-high dimension. Journal of Machine Learning Research, 23 (63): 1-47. 

Price, B. S., Allenbrand, C. & Sherwood, B. (2022). Detecting clusters in multivariate response regression. WIREs Computational Statistics, 14, (3): e1551.

Ghasemkhani, H., Goes, P. & Tripathi, A. K. (2022). Effect of market information on bidder attrition in online auction markets. MIS Quarterly, 46 (2): 1009–1034.

Tripathi, A. K., Lee, Y. J., & Basu, A. (2022). Analyzing the impact of public buyer–seller engagement during online auctions. Information Systems Research, 33 (4): 1264–1286.


Atal, S. Dutta, A., Abdelmoniem, A. M., Banerjee, T., Canini, M., & Kalnis, P. (2021). Rethinking gradient sparsification as total error minimization. Advances in Neural Information Processing Systems 34: 8133–8146

Banerjee T, Liu Q, Mukherjee G, and Sun W. (2021). A general framework for empirical Bayes estimation in discrete linear exponential family. Journal of Machine Learning Research. 22 (67): 1–46.

Banerjee T, Mukherjee G, & Paul D. (2021). Improved shrinkage prediction under a spiked covariance structure. Journal of Machine Learning Research. 22 (180): 1–40.

Chakraborty, S. & Swinney, R. (2021). Signaling to the crowd: Private quality information and rewards-based crowdfunding. Manufacturing & Service Operations Management. 23 (1): 155–169.

Lee, Y., Coyle, J., & Chen, A. N. K. (2021). Improving intention to back projects with effective designs of progress presentation in crowdfunding campaign sites. Decision Support Systems, 147: 113573.

Cao, Q., Chen, A. N. K., Ewing, B., & Thompson, M. (2021). Evaluating information system success and impact on sustainability practices: A survey and a case study of regional Mesonet information systems. Sustainability, 13: 7260.

Qiu, L., Chhikara, A., & Vakharia, A. (2021). Multidimensional observational learning in social networks: Theory and experimental evidence. Information Systems Research. 32 (3), 675–1097.

Dey, D., Ghoshal, A., & Lahiri, A. (2021). Support forums and software vendor’s pricing strategy. Information Systems Research, 32, (2): 653–659.

Gupta, A., Lash, M. T., & Nachimuthu, S. K. (2021). Optimal sepsis patient treatment using human-in-the-loop artificial intelligence. Expert Systems with Applications, 169: 114476.

Li, S., Zhu, X., Chen, Y., & Liu, D. (2021). PAsso: an R package for assessing partial association between ordinal variables, The R Journal. 13 (2): 239–252.

Liu, D., Li, S., Yu, Y., & Moustaki, I. (2021). Assessing partial association between ordinal variables: Quantification, visualization, and hypothesis testing. Journal of the American Statistical Association. 116 (534): 955–968.

Jiroušek, R., V. Kratochvìl, V., & Shenoy, P. P. (2021). Entropy-based learning of compositional models from data. In T. Denœux, E. Lefévre, Z. Liu, and F. Pichon (eds.), Belief Functions: Theory and Applications, Proceedings of the 6th International Conference, BELIEF 2021, Lecture Notes in Artificial Intelligence, Vol. 12915, pp. 117–126, Springer Nature, Switzerland.

Price, B. S., Molstad, A. J., & Sherwood, B. (2021). Estimating multiple precision matrices with cluster fusion regularization. Journal of Computational & Graphical Statistics, 30 (4): 823–834.

Jiang J., Srinivasan K. (2021). Comparing pregnancy and childbirth-related hospital visits in Arizona before and during COVID-19 using network analysis. Journal of Digital Science. 3 (2): 19–36.


Banerjee, T., Mukherjee, G., & Sun, W. (2020). Adaptive sparse estimation with side information. Journal of the American Statistical Association, 115 (220): 2053–2067.

Banerjee, T., Mukherjee, G., Dutta, S., & Ghosh, P. (2020). A large-scale constrained joint modeling approach for predicting user activity, engagement, and churn with application to freemium mobile games. Journal of the American Statistical Association, 115 (530): 538–554.

Liu, Y., Lee, Y. G., & Chen, A. N. K. (2020). How IT wisdom affects firm performance: An empirical investigation of 15-Year US panel data. Decision Support Systems, 133: 113300.

Denœux, T. & Shenoy, P. P. (2020). An interval-valued utility theory for decision making with Dempster-Shafer belief functions. International Journal of Approximate Reasoning, 124 (9): 194–216.

Jirousek, R. & Shenoy, P. P. (2020). On properties of a new decomposable entropy of Dempster-Shafer belief functions. International Journal of Approximate Reasoning, 119 (4): 260–279.

Shenoy, P. P. (2020). An expectation operator for belief functions in the Dempster-Shafer theory. International Journal of General Systems, 49 (1): 112–141.

Tan, Y. & Shenoy, P. P. (2020). A bias-variance based heuristic for constructing a hybrid logistic regression-naive Bayes model for classification. International Journal of Approximate Reasoning, 117 (2): 15–28.

Sherwood, B., Molstad, M., & Singha, S. (2020). Asymptotic properties of concave L1-norm group penalties. Statistics & Probability Letters, 157: 108631.

Srinivasan, K., et al. (2020). A novel fracture prediction model using machine learning in community-based cohort. Journal of Bone & Mineral Research, 4 (3): e10337.

Srinivasan, K., et al. (2020). Wellbuilt for wellbeing: Controlling relative humidity matters for our health. Indoor Air, 30 (1): 167–179.


Banerjee, T. & Mukherjee, G. (2019). Discussion of “CARS: Covariate assisted ranking and screening for large-scale two-sample inference” by Cai, Sun and Wang. Journal of the Royal Statistical Society, Series B, 81 (2): 223–224.

Banerjee, T., Bhattacharya, B. B., & Mukherjee, G. (2020). A nearest-neighbor based nonparametric test for viral remodeling in heterogeneous single-cell proteomic data. Annals of Applied Statistics, 14 (4): 1777–1805.

Lee, Y. & Chen, A. N. K. (2019). The effects of progress cues and gender on online wait. Decision Support Systems, 123: 113070.

Dey, D., Kim, A., & Lahiri, A. (2019). Online piracy and the ‘Longer Arm’ of enforcement. Management Science, 65 (3): 1173–1190.

Kim, A., Lahiri, A., Dey, D., & Kane G. C. (2019). ‘Just enough’ piracy can be a good thing. Sloan Management Review, 61 (1): 13–14.

Lash, M. T., Zhang, M., Zhou, X., Lynch, C. F., & Street, W. N. (2019). Deriving enhanced geographical representations via similarity-based spectral analysis: Predicting colorectal cancer survival curves in Iowa.  International Journal of Data Mining & Bioinformatics, 21 (3):183–211.

Lash, M. T., Slater, J., Polgreen, P. M., & Segre, A. M. (2019). 21 million opportunities: a 19 facility investigation of factors affecting hand-hygiene compliance via linear predictive models. Journal of Healthcare Informatics Research, 3 (4): 393–413.

Paul, A., Rajapakshe, T., & Mallik, S. (2019). Socially optimal contracting between a regional blood bank and hospitals. Production & Operations Management, 28 (4): 908–932.

Li, K., Wang, L., Chhajed, D., & Mallik, S. (2019). The impact of quality perception and consumer valuation change on manufacturer’s optimal warranty, pricing and market coverage strategies. Decision Sciences, 50 (2): 311–339.

Geng, Q. & Mallik, S. (2019). Managing television commercial inventory under competition: An equilibrium analysis. Decision Sciences, 50 (1): 170-201.

Jaunzemis, A. D., Holzinger, M. J., Chan, M. W., & Shenoy, P. P. (2019). Evidence gathering for hypothesis resolution using judicial evidential reasoning. Information Fusion, 49 (9): 26–45.

Denœux, T. & Shenoy, P. P., (2019). An axiomatic utility theory for Dempster-Shafer belief functions. In J. de Bock (ed.), Proceedings of the 2019 International Symposium on Imprecise Probabilities: Theory and Applications (ISIPTA-19), Proceedings of Machine Learning Research, Vol. 103, 145–155.


Arikan, M., Ata, B., Friedewald, J. J., & Parker, R. (2018). Enhancing kidney supply through geographic sharing in the United States. Production & Operations Management, 27 (12): 2103–2121.

Chen, A. N. K., Lee, Y. G., & Hwang, Y. (2018). Managing online wait: Designing effective waiting screens across cultures. Information & Management, 55 (5): 558–575.

Chen, W., Dawande, M., & Janakiraman, G. (2018). Optimal procurement auction under multi-stage supplier qualification. Manufacturing & Service Operations Management, 20 (3): 389–600.

Kim, A., Lahiri, A., & Dey, D. (2018). The ‘Invisible Hand’ of piracy: An economic analysis of the information-goods supply chain. MIS Quarterly, 42, (4): 1117–1141.

Lahiri, A. & Dey, D. (2018). Versioning and information dissemination: A new perspective. Information Systems Research, 29 (4): 965–983.

Schneider, M. J., Jagpal, S. Gupta, S. Li, S., & Yu, Y. (2018). A flexible method for protecting marketing data: An application to point-of-sale data. Marketing Science 37 (1): 153–171.

Jiroušek, R. & Shenoy, P. P. (2018). Combination and composition in probabilistic models. In L. H. Ahn, L. S. Dong, V. Kreinovich, and N. N. Thach (eds.), Econometrics for Financial Applications: ECONVN 2018 Conference Proceedings, Studies in Computational Intelligence, 760, 120–133, Springer, Cham.

Jiroušek, R. & Shenoy, P. P. (2018). A decomposable entropy of belief functions in the Dempster-Shafer theory. In S. Destercke, T. Denœux, F. Cuzzolin, and A. Martin (eds.), Belief Functions: Theory and Applications, Lecture Notes in Artificial Intelligence, 11069, 46–154, Springer Nature, Switzerland.

Jirousek, R., & Shenoy, P. P. (2018). A new definition of entropy of belief functions in the Dempster-Shafer theory. International Journal of Approximate Reasoning, 92(1): 4965.

Singha, S. & Shenoy, P. P. (2018). An adaptive heuristic for feature selection based on complementarity. Machine Learning, 107 (12): 2027–2071.

Wang, L., Zhou, Y., Song, R., & Sherwood, B. (2018). Quantile-optimal treatment regimes. Journal of the American Statistical Association, 113 (523): 1243–1254.

Price, B. S. & Sherwood, B. (2018). A Cluster elastic net for multivariate regression. Journal of Machine Learning Research, 18, 1–39.

Sherwood, B., et al. (2018). Genome-wide DNA methylation associations with spontaneous preterm birth in US blacks: Findings in maternal and cord blood samples. Epigenetics, 13 (2): 163–172.


Nicolae, M., Arikan, M., Deshpande, V., & Ferguson, M. (2017). Do bags fly free? An empirical analysis of the operational implications of airline baggage fees. Management Science, 63 (10), 3187–3206.

Banerjee, T., Mukherjee, G., & Radchenko, P. (2017). Feature screening in large scale cluster analysis. Journal of Multivariate Analysis, 161: 191–212.

Banerjee, T., et al. (2017). Mass cytometric analysis of HIV entry, replication, and remodeling in tissue CD4+ T cells. Cell Reports, 20 (4): 984–998.

Lee, Y. G., Chen, A. N. K., & Hess, T. (2017). The online waiting experience: Using temporal information and distractors to make online waits feel shorter. Journal of the Association for Information Systems, 18 (3): 231–263.

Ghoshal, A., Lahiri, A., & Dey, D. (2017). Drawing a line in the sand: Commitment problem in ending software support. MIS Quarterly, 41 (4): 1227–1247.

Singha, S., Hillmer, S., & Shenoy, P. P. (2017). On computing probabilities of dismissal of 10b-5 securities class-action cases. Decision Support Systems, 94 (2): 29–41.

Gerke, A. K., Tang, F., Lash, M. T., Schappet, J., Phillips, E., & Polgreen, P. M. (2017). A web-based registry for patients with sarcoidosis. Sarcoidosis Vasculitis & Diffuse Lung Diseases, 34 (1): 26–34.

Schneider, M. J., Jagpal, S. Gupta, S. Li, S., & Yu, Y. (2017). Protecting customer data: Marketing with second-party data. International Journal of Research in Marketing 34 (3): 593–603.

Ma, M. & Mallik, S. (2017). Bundling of vertically differentiated products in a supply chain. Decision Sciences, 48 (4): 625656.

Cobb, B. R. & Shenoy, P. P. (2017). Inference in hybrid Bayesian networks with nonlinear deterministic conditionals. International Journal of Intelligent Systems, 32 (12): 1217–1246.

Zhou, W., Sherwood, B., Ji, W., Du, F., Bai, J., & Ji, H. (2017). Genome-wide prediction of DNase I hypersensitivity using gene expression. Nature Communications, 8: Article 1038.

Sherwood, B., et al. (2017). Genome-wide approach identified a novel gene-maternal pre- pregnancy BMI interaction on preterm birth. Nature Communications, 8: Article 15608.


Liu, Y., Chen, A. N. K., & Sim, J. (2016). Does media exposure of firm IT practices matter to firm market value? American Journal of Engineering Research, 5 (9): 122–129.

Dey, D. & Lahiri, A. (2016). Versioning: Go vertical in a horizontal market? Journal of Management Information Systems, 33 (2): 546–572.

Lash, M. T. & Zhao, K. (2016). Early predictions of movie success: The who, what, and when of profitability. Journal of Management Information Systems, 33 (3):874–903.

Tan, Y., Shenoy, P. P.,Chan, M. W., & Romberg, P. M. (2016). On construction of hybrid logistic regression naïve Bayes model for classification. In A. Antonucci, G. Corani, & C. de Campos, Proceedings of the Machine Learning Research, Lecture Notes in Artificial Intelligence, 523–534.

Jiroušek, R. & Shenoy, P. P. (2016). Entropy of belief functions in the Dempster-Shafer theory: A new perspective. In J. Vejnarová & V. Kratochvíl, Belief Functions: Theory & Applications, Lecture Notes in Artificial Intelligence, 3–13.

Cinicioglu, E. N., & Shenoy, P. P. (2016). A new heuristic for learning Bayesian networks from limited datasets: A real-time recommendation system application with RFID systems in grocery stores. Annals of Operations Research, 244 (2): 385–405.

Jiroušek, R., & Shenoy, P. P. (2016). Causal compositional models in valuation-based systems with examples in specific theories. International Journal of Approximate Reasoning, 72 (1): 95–112.

Zhou, W., Sherwood, B., & Ji, H. (2016). Computational prediction of the global functional genomic landscape: Applications, methods and challenges. Human Heredity, 81 (2): 88–105.

Sherwood, B. (2016). Variable selection for additive partial linear quantile regression with missing covariates. Journal of Multivariate Analysis, 152: 206–223.

Sherwood, B., et al. (2016). Epigenome-wide association study links site-specific DNA methylation changes with cow's milk allergy. The Journal of Allergy & Clinical Immunology, 138 (3): 908–911.

Wang, L. & Sherwood, B. (2016). Discussion of “Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood” by Yunwen Yang, Huixia Judy Wang and Xuming He. International Statistical Review, 84 (3): 356–359.

Sherwood, B., Zhou, A., Weintraub, S., & Wang, L. (2016). Using quantile regression to create baseline norms for neuropsychological tests. Alzheimer's & Dementia: Diagnosis & Disease Monitoring, 2: 12–18

Sherwood, B. & Wang, L. (2016). Partially linear additive quantile regression in ultra-high dimension. Annals of Statistics, 44 (1): 288–317.


Gupta, S., Chen, W., Dawande, M., & Janakiraman, G. (2015). Optimal descending mechanisms for constrained procurement. Production & Operations Management. 24 (12): 1955–1965.

Dey, D., Lahiri, A., & Zhang, G. (2015). Optimal policies for security patch management. INFORMS Journal on Computing, 27, (3): 462–477.

Shenoy, P. P., Rumi, R., & Salmeron, A. (2015). Practical aspects of solving hybrid bayesian networks containing deterministic conditionals. International Journal of Intelligent Systems, 30 (3): 265–291.