Publications

Peer Reviewed Journal Articles: Published/In Press/Accepted

1.             Chakraborty, S., Huang Y., Dey, T., and Banerjee, A., (2025) “Federated Learning: Examining Statistical Operating Characteristics in the Context of Privacy-preserving Information Sharing,”  WIRE: Computational Statistics, 17(3), 1-13. Link

Journal Metrics: 5 year Impact factor: 6.1; Acceptance rates: NA.

2.             Chakraborty,  S., Cho., M., Dey, T., and Xue, H., (2025)   “BayesMultiomics: An R Package for Bayesian Shrinkage Models for Integration and Analysis of Multi-Platform High-Dimensional Genomics Data,” Statistical Analysis and Data Mining, 18(1), 1-5. Link

Journal Metrics: 5 year Impact factor: 2.1; Acceptance rates: 8%.

3.             Huang Y., Cho, M., Chakraborty, S., and Dey, T., (2025) “Variable Selection for Prediction in Clinical Research,”  WIRE: Computational Statistics, 17(2), 1-14. Link

Journal Metrics: 5 year Impact factor: 6.1; Acceptance rates: NA.

4.             Xue, H.,  Chakraborty,  S., and Dey, T., (2024), “Bayesian shrinkage models for integration and analysis of multiplatform high-dimensional genomics data,” Statistical Analysis and Data Mining, 17(2), 1-17. Link

Journal Metrics: 5 year Impact factor: 2.1; Acceptance rates: 8%.

5.             Huang, Y., Chakraborty,  S., Wu, X., Braun, D., Dominici, F and, Dey, T, (2025), “Scalable Survival Analysis: Equivalence of Cox and Log-Linear Models for Big Data ,” CHEST, 168(2), 295-297. Link

Journal Metrics: 5 year Impact factor: 9.1; Acceptance rates:  8.2%.

6.             Wang, Z., Chakraborty, S., and Wood, P.,  (2024), “Bayesian Elastic-Net and Fused Lasso for Semiparametric Structural Equation Models: An Application in Understanding the Relationship Between Alcohol Morbidity and Other Substance Abuse Factors Among American Youth,” Journal of the Indian Society for Probability and Statistics, Springer, 25(1), 1-26. Link

Journal Metrics: 5 year Impact factor: 0.6; Acceptance rates: 15%.

7.             Chakraborty, S., Dey, T., Xiang, L., and Adler, J.T., (2024), “A Spatial Gaussian-Process Boosting Analysis of Socioeconomic Disparities in Wait-Listing of End-Stage Kidney Disease Patients across the United States,” Stats (Special issue on "Bayes and Empirical Bayes Inference"), 7(2), 508-520. Link  

Journal Metrics: 5 year Impact factor: 1.1 ; Acceptance rates: 40%.

8.             Chakraborty, S., Dey, T., Jun, Y., Lim, C. Y., Mukherjee, A., & Dominici, F. (2022), “A Spatiotemporal Analytical Outlook of the Exposure to Air Pollution and COVID-19 Mortality in the USA,” Journal of Agricultural, Biological, and Environmental Statistics, 27(3), 419-439. Link. Journal Metrics: 5 year Impact factor: 1.4; Acceptance rates:  25%.

9.             Chakraborty, S., Menifield, C., and Daw, R., (2022), “Impact of Stand Your Ground, Background Checks and Conceal and Carry Laws on Homicide Rates in the U.S.A: Bayesian Change Point Analysis,” Journal of Public Management & Social Policy, 29(1). Link.

Journal Metrics: 5 year Impact factor: NA; Acceptance rates: 50%.

10.         Chakraborty, S., Zhao, P., Huang, Y., and Dey, T. (2022), “Semi Parametric Survival Analysis of 30-day Hospital Readmissions with Bayesian Additive Regression Kernel Model,” Stats, special issue on Survival Analysis: Models and Applications. 5(3), 617-630. Link

Journal Metrics: 5 year Impact factor: 1.1 ; Acceptance rates: 40%.

11.         Huang, Y., Darr, C., Gangopadhyay, K., Gangopadhyay, S., Bok, S., and Chakraborty, S., (2022), “Applications of Machine Learning Tools for Ultra-sensitive Detection of Lipoarabinomannan with Plasmonic Grating Biosensors in Clinical Samples of Tuberculosis,” PLoS One,  17(10). Link Journal Metrics: 5 year Impact factor: 3.2; Acceptance rates: 31%.

12.         Dey, T., Lee, J., Chakraborty, S., Chandra, J., Bhaskar, A., Zhang, K., Bhaskar, A., & Dominici, F. (2021), “Lag time between state-level policy interventions and change points in COVID-19 outcomes in the United States,” Patterns (New York, N.Y.), 2(8), 100306. Link

Journal Metrics: 5 year Impact factor: 7.7; Acceptance rates: NA.

13.         Chakraborty, S., Dey, T.,  Mukherjee, A., Alberts JL, and Linder SM, (2020), “Functional modeling of pedaling kinematics for the Stroke patients.” Journal of Biopharmaceutical Statistics, 30(4), 674-688.   Link

Journal Metrics: 5 year Impact factor: 1.3; Acceptance rates:  35%.

14.         Dey, T.,  Mukherjee, A., and Chakraborty, S.,  (2020), “A Practical Overview of Case Control Studies in Clinical Practice.”, CHEST, 158(1), 57-64. Link.

Journal Metrics: 5 year Impact factor: 9.1; Acceptance rates:  8.2%.

15.         Dey, T.,  and Mukherjee, A., and Chakraborty, S., (2020), “A Practical Overview and Reporting Strategies for Statistical Analysis of Survival Studies,” CHEST (Impact Factor: 9.65), 158(1), 39-48. Link

Journal Metrics: 5 year Impact factor: 9.1; Acceptance rates:  8.2%.

16.         Hoeksema, J.D., Bever, J.D., Chakraborty, S., Housworth, E., Lajeunesse, M., Meadow, J.F., Milligan, B., Rúa, M., Umbanhowar, and J., Viechtbauer, (2018),"Evolutionary history predicts the strength of mycorrhizal mutualism: A meta-analysis", Nature Communications Biology 1, Article number 116, 1-9. Link

Journal Metrics: 5 year Impact factor: 5.8; Acceptance rates:  30%.

17.         Chakraborty, S. and  Lozano, A.C., (2019), “Graph Laplacian Prior for Variable Selection and Grouping,” Computational Statsitics and Data Analysis, 136, August 2019, 72-91. Link

Journal Metrics: 5 year Impact factor: 1.9; Acceptance rates:  9%.

18.         Spencer, M., Takahashi, N., Chakraborty, S., Miles, J., and Shyu, C., (2018), “Heritable Genotype Contrast Mining Reveals Novel Gene Associations Specific to Autism Subgroups,” Journal of the American Medical Informatics Association, 77, 50-61. Link

Journal Metrics: 5 year Impact factor: 5.9; Acceptance rates:  18-20%.

19.         Lee, K.H., Chakraborty, S., and Sun, J., (2017), “Variable Selection for High-Dimensional Genomic Data with Censored Outcomes Using Group Lasso Prior,”   Computational Statsitics and Data Analysis, 112, 1-13.  Link

Journal Metrics: 5 year Impact factor: 1.9; Acceptance rates:  9%.

20.         Kargupta, R, Puttaswamy, S, Lee, A, Butler, T,  Chakraborty, S., Sengupta,S,  (2017), “Rapid culture-based detection of living Mycobacteria using microchannel Electrical Impedance Spectroscopy (m-EIS),” Biological Research, 50:21. Link.

Journal Metrics: 5 year Impact factor: 6.6; Acceptance rates:  NA. 

21.         Xu, C. and Chakraborty, S.,  (2017), “Bayesian Kernel Machine Models for Testing Genetic Pathway Effects in Prostate Cancer Prognosis,” Statistical Analysis and Data mining, 10(6), 378-392. Link

Journal Metrics: 5 year Impact factor: 2.1; Acceptance rates: 8%.

22.         Bhattacharya, S, Steele, R, Srivastava, S, Chakraborty, S, Bisceglie, A, and Ray,R.B, (2016), “Serum microRNA, miR-30e and miR-223, as novel non-invasive biomarkers for hepatocellular carcinoma,” The American Journal of Pathology, 186:2,242-247. Link

Journal Metrics: 5 year Impact factor: 4.8; Acceptance rates:  NA.

23.         Roy, V. and Chakraborty, S., (2016),  “Selection of Tuning Parameters, Solution Paths and Standard Errors for Bayesian Lassos,” Bayesian Analysis, 12:3, 753-778. Link

Journal Metrics: 5 year Impact factor: 3.8 ; Acceptance rates: 15-20%. 

24.         Lee, K.H., Chakraborty, S., and Sun, J., (2015) “Survival Prediction and Variable Selection with Simultaneous Shrinkage and Grouping Priors,” Statistical Analysis and Data Mining, 8(2), 114-127. Link  

Journal Metrics: 5 year Impact factor: 2.1; Acceptance rates: 8%.

25.         Liu, F., Chakraborty, S., Li, F., Liu, Y., and  Lozano, A.C.,  (2014), “Bayesian Regularization via Graph Laplacian,” Bayesian Analysis, 9:2, 449-474. Link

Journal Metrics: 5 year Impact factor: 3.8 ; Acceptance rates: 15-20%. 

26.         Chakraborty, S., Ghosh, M., and  Mallick, B.K. (2012), “Bayesian Non Linear Regression for Large p Small n Problems,” Journal of Multivariate Analysis, 108, 28–40. Link  

Journal Metrics: 5 year Impact factor:1.7; Acceptance rates:  17%.

27.         Chakraborty, S. (2012),  “Multivariate Bayesian Kernel Regression Model for High Dimensional Data and its Practical Applications in Near-Infrared (NIR) Spectroscopy,” Computational Statistics and Data Analysis, 56:9, 2742–2755. Link

Journal Metrics: 5 year Impact factor: 1.9; Acceptance rates:  9%.

28.         Puttaswamy, S., Lee,  B.D., Amighi, B., Chakraborty, S., Sengupta, S. (2012), “ Novel Electrical Method for the Rapid Determination of Minimum Inhibitory Con­centration (MIC) and Assay of Bactericidal/Bacteriostatic Activity,”  Journal of  Biosensors and  Bio­electronics,  S2:003. Link Journal Metrics: 5 year Impact factor: 2.53; Acceptance rates:  NA.

29.         Chakraborty, S. (2011), “Bayesian Semi-supervised Learning with Support Vector Machine,” Statistical Methodology (special edition on data mining), 8:1, 68-82. Link

Journal Metrics: 5 year Impact factor: 0.87; Acceptance rates:  NA.

30.         Chakraborty, S. and Guo, R. (2011), “A Bayesian Hybrid Huberized SVM and its Applications in High Dimensional Medical Data,” Computational Statistics and Data Analysis, 55:3, 1342-1356. Link

Journal Metrics: 5 year Impact factor: 1.9; Acceptance rates:  9%.

31.         Lee, K.H., Chakraborty, S., and Sun, J. (2011), “Bayesian Variable Selection in Semiparametric

Proportional Hazards Model for High Dimensional Survival Data,” The International Journal of Biostatistics, 7:1, 1-32. Link

Journal Metrics: 5 year Impact factor: 1.6; Acceptance rates: 21%.

32.         Khalilia, M., Chakraborty, S., and Popescu, M. (2011), “Predicting Disease Risks from Highly Unbalanced Data Using  Random Forest,” BMC Medical Informatics and Decision Making, 11:51. Link

Journal Metrics: 5 year Impact factor: 4.4; Acceptance rates:  NA.

33.         Mallick, B.K., Chakraborty, S., and Ghosh, M. (2011),  discussion paper of “Data Augmentation for Support Vector Machines, by Nicholas G. Polson and Steven L. Scott, ” invited article in Bayesian Analysis, 6:1, 25-30. Link

Journal Metrics: 5 year Impact factor: 3.8 ; Acceptance rates: 15-20%. 

34.        Guo, R. and Chakraborty, S. (2010), “Bayesian Adaptive Nearest Neighbor,” Statistical Analysis and Data mining, 3:2, 92-105. Link

Journal Metrics: 5 year Impact factor: 2.1; Acceptance rates: 8%.

35.        Lin, G.N., Cai, Z., Lin, G., Chakraborty, S., and Xu, D. (2009), “ComPhy: Prokaryotic Composite Distance Phylogenies Inferred from Whole-Genome Gene Sets,” BMC Bioinformatics, 10(Suppl 1):S5. Link

Journal Metrics: 5 year Impact factor: 3.5; Acceptance rates:  NA. 

36.        Chakraborty, S, Holan, S., and, McElroy, T. (2009), “A Bayesian Approach to Estimating the Long Memory Parameter,” Bayesian Analysis, 4:1, 159-190. Link

Journal Metrics: 5 year Impact factor: 3.8 ; Acceptance rates: 15-20%. 

37.         Chakraborty, S. (2009), “Simultaneous Cancer Classification and Gene Selection with Bayesian Nearest Neighbor Method: An Integrated Approach,” Computational Statistics and Data Analysis, 53:4, 1462-1474. Link

Journal Metrics: 5 year Impact factor: 1.9; Acceptance rates:  9%.

38.        Chakraborty, S. (2009), “Bayesian Binary Kernel Probit Model for Microarray Based Cancer Classification and Gene Selection,” Computational Statistics and Data Analysis, 53:2, 4198-4209. Link

Journal Metrics: 5 year Impact factor: 1.9; Acceptance rates:  9%.

39.        Chakraborty, S., Ghosh, M., Mallick, B.K., Ghosh, D., and Dougherty, E. (2007), “Gene Expression-Based Glioma Classification Using Hierarchical Bayesian Vector Machines,” Sankhya, 69:3, 514-547. Link

Journal Metrics: 5 year Impact factor: 0.9; Acceptance rates:  NA.

40.        Chakraborty, S., Ghosh, M., Maiti, T., and Tewari, A. (2005), “Bayesian Neural Networks for Bivariate Binary Data: An Application to Prostate Cancer Study,” Statistics in Medicine, 24, 3645-3662. Link

Journal Metrics: 5 year Impact factor: 2.6; Acceptance rates: 27%.

41.        Ghosh, M., Maiti, T., Kim, D., Chakraborty, S., and Tewari, A. (2004), “Hierarchical Bayesian Neural Networks: An Application to a Prostate Cancer Study,” Journal of the American Statistical Association, 99:467, 601-608. Link

Journal Metrics: 5 year Impact factor: 4.8; Acceptance rates:  13%.

Peer Reviewed Journal Articles: Submitted/Under Revision

42.         Das, P.. Dey, T., Peterson, C.B., and Chakraborty,  S.  “B-MASTER: Scalable Bayesian Multivariate Regression Analysis for Selecting Targeted Essential Regressors to Identify the Key Genera in Microbiome-Metabolite Relation Dynamics,” Under Review.

43.         Huang Y., Chakraborty, S., Dey, T., Hu, H., and Banerjee, A., “Privacy Preserving Statistical Inference with Heterogeneous Biomedical Data,”  Under Review.

44.         Jun, Y.,  Majumder, S., Chakraborty,  S., Lim, C., and Dey, T.,  “BSTZINB: A Bayesian Framework for Negative-Binomial Modeling of Spatio-Temporal Zero-Inflated Count Data in Epidemiology,”  Under Review.

45.         Chakraborty,  S., Bardhan., S., Gullapalli, S., and Khadka, S., “Forecasting Oak Diameter Using an LSTM Neural Network in the Missouri State of USA,” Under Review.

46.         Patil, A.,  Syam, N., Chakraborty,  S., and Krishnamurthy, P.,  “Team Performance Diversity and the Problem of Social Comparison: An Empirical Investigation of their Impact on Salespeople's Performance,” Under 1st Revision.

Peer Reviewed Invited Book Chapters: Published/In Press/Accepted

47.         Cook, T., Brumley, D., and Chakraborty,  S. (2024) “Pathway and Gene Selection with Guided Regularized Random Forests,” Springer Book on Big Data Analysis, Biostatistics and Bioinformatics, Springer. Invited Article. Accepted for Publication.

48.         Wang, Z., Chakraborty, S., and Ray, B.,  (2024), “Bayesian Kernel Based Modeling and Selection of Genetic Pathways and Genes for Cancer Studies in Personalized Treatment and Medicine,” Springer Book on Big Data Analysis, Biostatistics and Bioinformatics, Springer. Invited Article. Accepted for Publication.

49.         Chakraborty, S. (2016),  “Bayesian Additive Regression Tree for Seemingly Unrelated Regression with Automatic Tree Selection” Handbook of Statistics, Volume 35, Cognitive Computing: Theory and Applications, 229-251. Link

50.         Chakraborty, S., Mallick, B.K. , and Ghosh, M. (2013), “Bayesian Hierarchical Kernel Machines for Nonlinear Regression and Classification,” invited chapter,  in Bayesian Theory and Applications (A tribute to Sir Adrian Smith), edited by Paul Damien, Petros Dellaportas, Nicholas G. Polson and David A. Stephens, Oxford University Press, 50-69. Link

51.         Chakraborty, S. and Ghosh, M. (2012), “Applications of Bayesian Neural Networks in Prostate cancer Study,” Invited chapter in Handbook of Statistics, Volume 28: Bioinformatics, North Holland, 241-262. Link

 52.        Chakraborty, S. (2011),  “An Intermediate Course in Probability by Allan Gut,”  Invited book review for Journal of the American Statistical Association, 106(495), 1221. Link

Journal Metrics: 5 year Impact factor: 4.8; Acceptance rates:  13%.

53.         Chakraborty, S. (2014),  “Bayesian Essentials with R by Jean-Michel Marin and Christian P. Robert,”  Invited book review for International Statistical Review, 82(3), 488-490. Link

Journal Metrics: 5 year Impact factor: 1.8; Acceptance rates: 18%.

Book Under Preparation

54.   Bayesian Methods and Models in Machine Learning and Data Mining, by Sounak Chakraborty, CRC Press, Taylor and Francis Group, under contract, tentative date of delivery of manuscript Dec 2025.