Analytics, Information, Operations publications
2025 and forthcoming
Ahuja, V., Alan, Y., & Arikan, M. (2025). The role of route-level decisions in the efficiency and resilience of airline operations: Evidence from the Wright Amendment repeal. Manufacturing & Service Operations Management. 27 (2): 339–678.
Banerjee, T., & Sharma, P. (2025). Nonparametric empirical bayes prediction in mixed models: Statistics and Computing, 35(5), 145.
Gang, B., & Banerjee, T. (2025). Large-scale multiple testing of composite null hypotheses under heteroskedasticity. Biometrika. 112 (2): asaf007.
Luo, J., Banerjee, T., Mukherjee, G., & Sun, W. (in press). Empirical Bayes estimation with side information: A nonparametric integrative Tweedie approach. Statistica Sinica.
Sharma, P., & Banerjee, T. (in press). Do financial regulators act in the public's interest? A Bayesian latent class estimation framework for assessing regulatory responses to banking crises. Journal of the Royal Statistical Society: Series A.
Lee Y., Chen, A. N. K., & Wang, W. (in press). Push it cross the finish line – Designing online interface to induce choice closure at the post-decision pre-purchase stage. Information Systems Research.
Dey, D., Lahiri, A., & Mukherjee, R. (2025). Polarization or bias: Take your click on social media. Journal of the Association for Information Systems. 26 (3): 850–878.
Dey, D., & Lahiri, A. (in press). "Extortionality" in ransomware attacks: A microeconomic study of extortion and externality. Information Systems Research.
Amaya, J. & Reed, S. (2025). Space management policy for urban last-mile Parking infrastructure: A demand-oriented approach. Transportation Research Part E: Logistics and Transportation Review, 200:104185.
Reed, S. (in press). Parking in routing last-mile deliveries. To appear in Encyclopedia in Operations Management. Elsevier.
Jiang, J., Bandeli, K. K., & Srinivasan K. (2025). Dynamic model selection in enterprise forecasting systems using sequence modeling. Decision Support Systems. 193: 114439.
Rao S., Juma N., Srinivasan K. (2025). Textual analysis of sustainability reports: Topics, firm value, and the moderating role of assurance. Journal of Risk and Financial Management. 18(8), 463.
2024
Banerjee, T., Bhattacharya, B. B. and Mukherjee, G., (2024). Bootstrapped edge count tests for nonparametric two-sample inference under heterogeneity. Journal of Computational & Graphical Statistics. 34 (1): 306–317.
Boyaci, T., Chakraborty, S., & Gurkan, H. (2024). Persuading skeptics and fans in the presence of additional information. Production & Operations Management. 33 (5): 1142–1154.
Garg, A., Demirezen, E. M., Dogan, K., & Cheng, H. K. (2024). Financial sustainability of IoT platforms: The role of quality and security. Production & Operations Management. 33 (2): 412–431.
Abbasi-Pooya, A., & Lash, M. T. (2024). The third party logistics provider freight management problem: A framework and deep reinforcement learning approach. Annals of Operations Research. 339 (1): 965–1024
Lash, M. T. (2024). HEX: Human-in-the-loop explainability via deep reinforcement learning. Decision Support Systems. 187: 114304.
Zhu, X., Li, S., Srinivasan, K., & Lash, M. T. (2024). Impact of the COVID-19 pandemic on the stock market and investor online word of mouth. Decision Support Systems. 176: 114074.
Li, S., Fan, Z., Liu, I., Morrison, P. S., & Liu, D. (2024). Surrogate method for partial association between mixed data with application to well-being survey analysis. Annals of Applied Statistics. 18 (3): 2254–2276.
Reed, S., Campbell, A., & Thomas, B. (2024) Does parking matter? The impact of parking time on last-mile delivery optimization. Transportation Research Part E: Logistics and Transportation Review 181: 103391.
Sherwood, B. & Price, B.S. (2024). On the use of minimum penalties in statistical learning. Journal of Computational & Graphical Statistics. 33 (1): 138–151.
Tan, Y., Sherwood, B., & Shenoy, P. P. (2024). A naive Bayes regularized logistic regression estimator for low-dimensional classification. International Journal of Approximate Reasoning. 172: 109239.
Tan, Y., Shenoy, P. P., Sherwood, B., Shenoy, C., Gaddy, M., & Oehlert, M. E. (2024). Bayesian network models for PTSD screening in veterans. INFORMS Journal on Computing. 36 (2): 495–509.
Srinivasan K., Currim, F., Ram, S. (2024). A reduced modeling approach for making predictions with incomplete data having blockwise missing patterns. INFORMS Journal of Data Science. 4 (1): 85–99.
2023
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.
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.
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.
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.
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.
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.
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.
Srinivasan, K. (2023). Graph data management, modeling, and mining. In Encyclopedia of Data Science & Machine Learning. IGI Global.
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.
2022
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.
2021
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.
2020
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.
2019
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.
2018
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): 49–65.
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.
2017
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): 625–656.
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.
2016
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.
2015
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.