Analytics, Information and Operations Management publications


Overview

2021 and forthcoming

Banerjee T, Liu Q, Mukherjee G and Sun W. A General Framework for Empirical Bayes Estimation in Discrete Linear EsponentialFamily. Journal of Machine Learning Research. (2021) 22(67):1-46.

Banerjee T, Mukherjee G and Paul D. Improved Shrinkage Prediction Under a Spiked Covariance Structure. Journal of Machine Learning Research. (2021) 22(180): 1-40.

Bernstein, F., Chakraborty, S., & Swinney, R. (in press). Intertemporal Content Variation with Customer Learning. Manufacturing & Service Operations Management.

Lee, Y., Coyle, J., and Chen, A. N. K., (2021). Improving Intention to Back Projects with Effective Designs of Progress Presentation in Crowdfunding Campaign Sites. Decision Support Systems, 147, Article 113573, 1–12.

Cao, Q., Chen, A. N. K., Ewing, B., and 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, Article 7260, 1–23.

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

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, 1-14.

Li, S., Zhu, X., Chen, Y., and Liu, D. (2021). PAsso: an R package for Assessing Partial Association between Ordinal Variables, The R Journal, (forthcoming).

Reed, S., Campbell, A. M., & Thomas, B. W. (2021). The Value of Autonomous Vehicles for Last-Mile Deliveries in Urban Environments. Management Science.

Jiroušek, R., V. Kratochvìl, and P. P. Shenoy, "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, 2021, Springer Nature, Switzerland.

Price, B. S., Molstad, A. J., & Sherwood, B. (2021). Estimating Multiple Precision Matrices with Cluster Fusion Regularization. Journal of Computational and Graphical Statistics, 1--30.

Price, B. S., Allenbrand, C., & Sherwood, B. (2021). Detecting clusters in multivariate response regression. Wiley Interdisciplinary Reviews: Computational Statistics, e1551.

2020

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, 1-11.

Banerjee, T., Mukherjee, G., & Sun, W. (2020). Adaptive Sparse Estimation with Side Information. Journal of American Statistical Association, Theory & Methods.115, 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 American Statistical Association, Applications & Case Studies.115, 538-554.

Chakraborty, S., & Swinney, R. Signaling to the Crowd: Private Quality Information and Rewards- Based Crowdfunding. Manufacturing & Service Operations Management. 23(1), 155-169.

Liu, D., Li, S., Yu, Y., & Moustaki, I. Assessing Partial Association between Ordinal Variables: Quantification, Visualization, and Hypothesis Testing. Journal of the American Statistical Association. 116(534), 955-968.

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.

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.

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

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

Kong, S. H., Ahn, D., Kim, B., Kim, J. H., Srinivasan, K., et al. (2020). A Novel Fracture Prediction Model Using Machine Learning in Community- Based Cohort. Journal of Bone and Mineral Research (IF:6.28), 6(28).

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, 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 (forthcoming) Healthcare Informatics Research, 3(4), 393-413.

Lash, M. T., . Zhang, X. Zhou, C.F. Lynch, and W.N. Street, \Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa", International Journal of Data Mining and Bioinformatics (IJDMB), 21(3):183-211, 2019.

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 and 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.

Lee, Y., & Chen, A. N.K. (2019). The Effects of Progress Cues and Gender on Online Wait. Decision Support Systems, 123, 1-13.

Jaunzemis, A. D., M. J. Holzinger, M. W. Chan, and Shenoy, P. P.,  (2019). “Evidence Gathering for Hypothesis Resolution Using Judicial Evidential Reasoning,” Information Fusion, 49(9), 26–45

Denœux, T. and 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.

Razjouyan, J., Lee, H., Nyugen, H., Lindberg, C., Srinivasan, K., et al. (2019). Wellbuilt for wellbeing: Why controlling relative humidity matters for our health? Indoor Air (IF:2.55), 2(55).

2018

Arikan, M., Ata, B., Friedewald, J. J., & Parker, R. (2018). Enhancing Kidney Supply Through Geographic Sharing in the United States. Production and 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, 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.

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. and 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. and 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.

Singha, S., & Shenoy, P. P., (2018) “An Adaptive Heuristic for Feature Selection Based on Complementarity,” Machine Learning, 107(12), 2027–2071.

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.

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

Price, B. S. and Sherwood, B. (2018) A Cluster Fusion Penalty for Grouping Response Variables in Multivariate Regression Models. Journal of Machine Learning Research, 18, 1-39.

Hong, X., Sherwood, B., Ladd-Acosta, C., Peng, S., Ji, H., Hao, K., Burd, I., Bartell, T.R., Wang, G., Tsai, H.J., Liu, X., Ji, Y., Wahl, A., Caruso, D., Lee-Parritz, A., Zuckerman, B., and Wang, X. (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(0), 3187-3206.

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

Cavrois, M., Banerjee, T., Mukherjee, G., Raman, N., Hussien, R., Rodriguez, B. A., ... & Ochsenbauer, C. (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., & 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 (JAIS), 18(3), 231–263.

A.K. Gerke, F. Tang, Lash, M. T., J. Schappet, E. Phillips and P.M. Polgreen, \A web-based registry
for patients with sarcoidosis", Sarcoidosis vasculitis and diffuse lung diseases (SVDLD), 34(1):26-34, 2017.

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

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

W., Sherwood, B., Ji, W., Du, F., Bai, J. and Ji, H. (2017) Genome-wide prediction of DNase I hypersensitivity using gene expression. Nature Communications, 8, Article 1038, 1-17.

Hong, X., Hao, K., Ji, H., Peng, S., Sherwood, B., Di Narzo, A., Tsai, H-J., Liu, X., Burd, I., Wang, G., Ji, Y., Caruso, D., Mao, G., Bartell, T., Zhang, Z., Pearson, C., Heffner, L., Cerda, S., Beaty, T., Fallin, M., Lee-Parritz, A., Zuckerman, B., Weeks, D. and Wang, X. (2017) Genome-wide approach identified a novel gene-maternal pre- pregnancy BMI interaction on preterm birth. Nature Communications, 8:15608; 1-10.

2016

Liu, Y., Andrew Chen, and J. Sim, “Does Media Exposure of Firm IT Practices Matter to Firm Market Value?” American Journal of Engineering Research, 5(9), 2016, 122–129.

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

Tan, Y., P.P. Shenoy, 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., & P.P. Shenoy, (2016). Entropy of Belief Functions in the Dempster-Shafer Theory: A New Perspective. In J. Vejnarová & V. Kratochvíl, Belief Functions: Theory and Applications, Lecture Notes in Artificial Intelligence, 3-13.

Cinicioglu, E. N., & P.P. Shenoy, (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., & P.P. Shenoy, (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. and Ji, H. (2016) Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods and Challenges. Human Heredity, 81, 88-105.

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

Hong, X., Ladd-Acosta, C., Hao, K., Sherwood, B., Ji, H., Keet, C.A., Kumar, R., Caruso, D., Liu, X., Wang, G., Chen, Z., Ji, Y., Mao, G., Walker, S.O., Bartell, T.R., Ji, Z., Sun, Y., Tsai, H-J., Pongracic, J.A., Weeks, D.E. and Wang, X. (2016) Epigenome-wide association study links site-specific DNA methylation changes with cow's milk allergy. The Journal of Allergy and Clinical Immunology, 138, 908-911.

Wang, L. and 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, 356-359.

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

Sherwood, B. and Wang, L. (2016). Partially linear additive quantile regression in ultra-high dimension. Annals of Statistics, 44 288-317. (supplementary material, implementation code)

2015

Gupta, S., Chen, W., Dawande, M., & Janakiraman, G. (2015). Optimal Descending Mechanisms for Constrained Procurement. Production and Operations Management. 24(12), 1955-1965.

P.P. Shenoy, Rumi, R., & Salmeron, A. (2015). Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals. International Journal of Intelligent Systems, 30(3), 265--291.