Manuscripts that are under review or in revision may not appear here.

Software:

  1. [M. Heyman and S. Chatterjee ] R package WiSEBoot, on wild, scale-enhanced bootstrap, the CRAN webpage for this package.
  2. [ S. Chatterjee ] R package UStatBookABSC, related to U-Statistics, M-Estimators and Resampling, the CRAN webpage for this package.

Articles:

  1. [A. Braverman, S. Chatterjee , M. Heyman and N. Cressie] Probabilistic Evaluation of Competing Climate Models, to appear in Advances in Statistical Climatology, Meteorology and Oceanography.
  2. [S. Agrawal, G. Atluri, A. Karpatne, W. Haltom, S. Liess, S. Chatterjee , and Vipin Kumar] Tripoles: A New Class of Relationships in Time Series Data, accepted for publication in Knowledge Discovery and Data Mining (KDD) 2017 conference proceedings.
  3. [A. Ermagun, S. Chatterjee , and D. Levinson] Using temporal detrending to observe the spatial correlation of traffic, PLOS ONE 12(5): e0176853, the article webpage.
  4. [S. Liess, S. Agrawal, S. Chatterjee , and Vipin Kumar] A Teleconnection between the West Siberian Plain and the ENSO Region, Journal of Climate, 30(1), 301-315, the article webpage.
  5. [N. , Y. Louzoun, L. Gragert, M. Maiers, S. Chatterjee , and M. Albrecht] Power Laws for Heavy-Tailed Distributions: Modeling Allele and Haplotype Diversity for the National Marrow Donor Program, PLOS Computational Biology 11(4): e1004204, the article webpage.
  6. [S. Majumdar, L. Dietz and S. Chatterjee ] Identifying Driving Factors Behind Indian Monsoon Precipitation using Model Selection based on Data Depth, Fifth International Workshop on Climate Informatics, the article webpage.
  7. [L. Dietz and S. Chatterjee ] Spatio-temporal hypothesis testing in model residuals, Fifth International Workshop on Climate Informatics, the article webpage.
  8. [U. Mukherjee, S. Majumdar, and S. Chatterjee ] Fast and Robust Supervised Learning in High Dimensions Using the Geometry of the Data, In: Perner P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science, vol 9165, the article webpage.
  9. [L. Dietz and S. Chatterjee ] Investigation of Precipitation Thresholds in the Indian Monsoon Using Logit-Normal Mixed Models, In: Lakshmanan V., Gilleland E., McGovern A., Tingley M. (eds) Machine Learning and Data Mining Approaches to Climate Science, the article webpage.
  10. [M. Heyman and S. Chatterjee ] Predicting Crop Yield via Partial Linear Model with Bootstrap, In: Lakshmanan V., Gilleland E., McGovern A., Tingley M. (eds) Machine Learning and Data Mining Approaches to Climate Science, the article webpage.
  11. [M. Bhattacharjee and S. Chatterjee ] On Bayesian Spatio-Temporal Modeling of Oceanographic Climate Characteristics, In: Current Trends in Bayesian Methodology with Applications Edited by Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey and Appaia Loganathan Chapman and Hall/CRC 2015 Pages 103–121, the article webpage.
  12. [A. Goncalves, V. Sivakumar, S. Chatterjee, D. Kumar, S. Chatterjee , A. Ganguly, V. Kumar, S. Liess, P. Ravikumar and A. Banerjee ] Robustness and synthesis of Earth System Models: a multi-task learning perspective, Fourth International Workshop on Climate Informatics, the article webpage.
  13. [U. Mukherjee and S. Chatterjee ] Fast algorithm for computing weighted projection quantiles and data depth for high-dimensional large data clouds, In: 2014 IEEE International Conference on Big Data, the article webpage.
  14. [ K. Monsen , S. Chatterjee , J. Timm, J. Poulsen and D, McNaughton] Factors Explaining Variability in Health Literacy Outcomes of Public Health Nursing Clients, Public Health Nursing, 32 (2), 94–100, March/April 2015, the article webpage.
  15. [ A. R. Ganguly, E. A. Kodra, A. Agrawal, A. Banerjee, S. Boriah, S. Chatterjee , S. Chatterjee, A. Choudhary, D. Das, J. Faghmous, P. Ganguli, S. Ghosh, K. Hayhoe, C. Hays, W. Hendrix, Q. Fu, J. Kawale, D. Kumar, V. Kumar, W. Liao, S. Liess, R. Mawalagedara, V. Mithal, R. Oglesby, K. Salvi, P. K. Snyder, K. Steinhaeuser, D. Wang and D. Wuebbles ] Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques, Nonlinear Processes in Geophysics, 21, 777–795, 2014, the article webpage.
  16. [ Y. Lu and S. Chatterjee ] Instability and change detection in exponential families and generalized linear models, with a study of Atlantic tropical storms, Nonlinear Processes in Geophysics, 21, 1133–1143, 2014, the article webpage.
  17. [ L. Dietz and S. Chatterjee ] Logit-normal mixed model for Indian monsoon precipitation, Nonlinear Processes in Geophysics, 21, 939–953, 2014, the article webpage.
  18. [ U. Mukherjee and S. Chatterjee ] A Fay-Herriot type approach for better prediction in multi-indexed response with application to Arctic seawater data analysis, Journal of the Indian Society of Agricultural Statistics, 68 (2), (2014), 257 -- 272.
  19. [ D. Das, A. Ganguly, S. Chatterjee , V. Kumar and Z. Obradovic] Spatially penalized regression for dependence analysis of rare events: A study in precipitation extremes, 2012 IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), the article webpage.
  20. [ J. H. Faghmous, M. Le, M. Uluyol, V. Kumar and S. Chatterjee ] A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics, 2013 IEEE 13th International Conference on Data Mining (ICDM), 151-160, the article webpage.
  21. [ A. R. Ganguly, E. Kodra, S. Chatterjee , A. Banerjee and H. N. Najm] Computational Data Sciences for Actionable Insights on Climate Extremes and Uncertainty, Chapter 5, page 127-158. In Computational Intelligent Data Analysis for Sustainable Development, Edited by T. Yu, S. Simo , N. Chawla, CRC Press, 440 pages, the article webpage.
  22. [ Z. Li, P. Qiu, S. Chatterjee and Z. Wang] Using p-values to design statistical process control charts, Statistical Papers, 54, 2013, 523--539, the article webpage.
  23. [ X. C. Chen, K. Steinhaeuser, S. Boriah, S. Chatterjee and V Kumar] Contextual Time Series Change Detection, Proceedings of the 2013 SIAM International Conference on Data Mining (SDM), the article webpage.
  24. [ J. Kawale, S. Chatterjee , D. Ormsby, K. Steinhaeuser, S. Liess and V. Kumar] Testing the significance of spatio-temporal teleconnection patterns, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 642-650 , the article webpage.
  25. [ S. Chatterjee, K. Steinhaeuser, A. Banerjee, S. Chatterjee and A. Ganguly] Sparse Group Lasso: Consistency and Climate Applications, Proceedings of the 2012 SIAM International Conference on Data Mining (SDM), the article webpage.
  26. [ J. Kawale, S. Chatterjee , A. Kumar, S. Liess, M. Steinbach and V. Kumar] Anomaly construction in climate data: Issues and challenges, Proceedings of the 2011 Conference on Intelligent Data Understanding, CIDU 2011, 189-203, the article webpage.
  27. [S. Chatterjee, A. Banerjee, S. Chatterjee and A. Ganguly] Sparse Group Lasso for Regression on Land Climate Variables, 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), the article webpage.
  28. [E. Kodra, S. Chatterjee and A. Ganguly] Challenges and opportunities toward improved data-guided handling of global climate model ensembles for regional climate change assessments, ICML 2011 Workshop on Machine Learning for Global Challenges.
  29. [A. Agovic, A. Banerjee and S. Chatterjee] Probabilistic matrix addition, Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 1025-1032, the article webpage.
  30. [N. Mukhopadhyay and S. Chatterjee] High dimensional data analysis using multivariate generalized spatial quantiles, Journal of Multivariate Analysis, 102 (4), (2011), 768 - 780, the article webpage.
  31. [E. Kodra, S. Chatterjee and A. Ganguly] Exploring Granger causality between global average observed time series of carbon dioxide and temperature, Theoretical and applied climatology, 104 (3), (2011), 325-335, the article webpage.
  32. [ S. Chatterjee and P. Qiu] Distribution-free cumulative sum control charts using bootstrap-based control limits,, Annals of Applied Statistics, 3(1) (2009), 349 - 369, the article webpage.
  33. [N. Mukhopadhyay and S. Chatterjee] Reply to 'Comment on causality and pathway search in microarray time series experiment', Bioinformatics, 24 (7), April 2008, Page 1033, the article webpage.
  34. [ S. Chatterjee and N. Mukhopadhyay] Risk and resampling under model uncertainty, Institute of Mathematical Statistics Collections, Volume 3, 2008, 155-169 the article webpage.
  35. [ S. Chatterjee and N. Mukhopadhyay] Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models, The Annals of Statistics, 36, 1221-1245, the article webpage.