Manuscripts that are under review or in revision may not appear here.
Software:
-
[M. Heyman and S. Chatterjee ]
R package WiSEBoot, on wild, scale-enhanced bootstrap,
the CRAN webpage for this package.
-
[ S. Chatterjee ]
R package UStatBookABSC, related to U-Statistics, M-Estimators and Resampling,
the CRAN webpage for this package.
Articles:
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[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.
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[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.
-
[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.
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[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.
-
[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.
-
[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.
-
[L. Dietz and
S. Chatterjee ]
Spatio-temporal hypothesis testing in model residuals,
Fifth International Workshop on Climate Informatics,
the article webpage.
-
[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.
-
[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.
-
[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.
-
[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.
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[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.
-
[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.
-
[ 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.
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[ 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.
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[ 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.
-
[ L. Dietz and S. Chatterjee
]
Logit-normal mixed model for Indian monsoon precipitation,
Nonlinear Processes in Geophysics, 21, 939–953, 2014,
the article webpage.
-
[ 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.
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[ 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.
-
[ 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.
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[ 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.
-
[ 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.
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[ 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.
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[ 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.
-
[ 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.
-
[ 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.
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[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.
-
[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.
-
[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.
-
[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.
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[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.
-
[ 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.
-
[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.
-
[ S. Chatterjee and N. Mukhopadhyay]
Risk and resampling under model uncertainty,
Institute of Mathematical Statistics Collections, Volume 3, 2008, 155-169
the article webpage.
-
[ 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.