Professor of Statistics.
I am a Professor in the Statistics department
at the University of Michigan . I graduated from
the Department of Statistics at the University
of Washington , Seattle, under the supervision of Professor Jon
A. Wellner . Prior to graduate study at the University of Washington,
I was a student at the Indian Statistical
Institute , where I received my Bachelors (B.Stat.) and Masters (M.Stat.)
in Statistics with specialization in Mathematical Statistics and Probability. For my CV, click here
Apart from Statistics I have strong (and often distracting) interests in history (including prehistory and ancestral genetics), classical music, philosophy, gourmet cuisine and single malt.
Papers Under Review:
Patra, R., Banerjee, M. and Michailidis, G. (2020) – A semi-parametric model for target localization in distributed systems. Paper
Eftekhari, H., Banerjee, M. and Ritov, Y. (2020) – Design of c-optimal experiments for high dimensional linear models. Paper
Eftekhari, H., Mukherjee, D., Banerjee, M. and Ritov, Y. (2020) – Markovian And Non-Markovian Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic. Paper
Maity, S., Sun, Y. and Banerjee M. (2020) – Minimax Optimal Approaches to the Label Shift Problem. Paper
Mukherjee, D., Banerjee, M. and Ritov, Y. (2020): Optimal Linear Discriminators for the Discrete Choice Model in Growing Dimensions. Paper
Lu, Z., Banerjee, M. and Michailidis, G. (2020): Intelligent Sampling and Inference for Multiple Change Points in Extremely Long Data Sequences. Paper
Core Papers (Published/Accepted):
Eftekhari, H., Banerjee, M. and Ritov, Y. (2021): Inference in High Dimensional Single Index Models under Symmetric Design. To appear in JMLRPaper
Mukherjee, D., Yurochkin, M., Banerjee, M. and Sun, Y. (2020) – Two Simple Ways To Learn Individual Fair Metrics From Data. ICML Proceedings. Paper.
Bhattacharjee, M., Banerjee, M. and Michailidis, G. (2020): Change Point Estimation in a Dynamic Stochastic Block Model. Paper JMLR, 21(107):159, 2020.
Mallik, A., M., Banerjee, M. and Michailidis, G. (2020): M-estimation in multistage sampling procedures. Paper
Sankhya, Series A, Vol. 82, 261-309. Special Volume in Memory of Professor J. K. Ghosh
Banerjee, M. and Durot, C. (2019): Circumventing Superefficiency: an Effective Strategy For Distributed Computing in Nonstandard Problems. Paper. EJS, Number 1, 1926-1977.
Zhei, F., Zhu, J., Banerjee, M. and Li, Y. (2019): Drawing inferences for High‐dimensional Linear Models: A Selection‐assisted Partial Regression and Smoothing Approach. Paper Biometrics, Vol. 75 (2), 551-561. Online link
Banerjee, M., Durot, C. and Sen, B. (2019): Divide and Conquer in Non-standard Problems and the Super-Efficiency Phenomenon. Paper Annals of Statistics, Volume 47, Pages 720--757.
Mallik, A., Banerjee, M. and Woodroofe, M. (2018): Baseline zone estimation in two dimensions with replicated measurements under a convexity constraint. Paper Statistica Sinica, Vol. 28, 1481-1502.
Lin, J., Basu, S., Banerjee, M. and Michailidis, G. (2016): Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models. JMLR,
Volume 17, 146, 1--51. Paper
Bagchi, P., Banerjee, M. and Stoev, S. (2016) -- Inference for monotone trends under short and long range dependence: Confidence intervals and new universal limits.
JASA, Volume 111:516, 1634--1647. Paper
Das, R., Banerjee, M., Nan, B. and Zheng, H. (2016) -- Fast estimation of regression parameters in a broken-stick model for longitudinal data. JASA,
Volume 111, 1132--1143. Paper
Song, R., Banerjee, M., and Kosorok, M. (2016) -- Asymptotics for change--point models under varying degrees of mis-specification.
Annals of Statistics 44.1, 153-182. Paper
Mallik, A., Sen, B., Banerjee, M. and Michailidis, G. (2016) -- Asymptotics for p-value based threshold estimation in dose-response settings. JSPI, Volume 174,
Tang, R., Banerjee, M., Michailidis, G., Mankad, S. (2015) -- Two stage plans for estimating the inverse of a monotone function. Technometrics, 57(3), 395-407. Paper
Mankad, S. Michailidis, G. and Banerjee, M. (2015) -- Threshold Value Estimation using Two-stage Plans in R. Journal of Statistical Software, 67.1 (2015): 1-19.
Banerjee, M. and Richardson, T. (2013) -- Exchangeable Bernoulli random variables and Bayes' postulate. Electronic Journal of Statistics, Volume 7,
Mallik, A., Banerjee, M. and Sen, B. (2013) -- Asymptotics for p-value based threshold estimation in regression settings. Electronic Journal of Statistics,
Volume 7, 2477--2515.Paper
Tang, R., Banerjee, M. and Kosorok, M. (2012) -- Asymptotics for current status data under varying observation time sparsity. Annals of Statistics,
Volume 40, Number 1 (2012), 45-72. Paper
Alonso-Garcia, J., Mateo, M., Sen,B., Banerjee, M., Catelan, M., Minniti, M., and von Braun, K. (2012) -- Uncloaking globular clusters in the inner Galaxy. The Astronomical Journal,
Alonso-Garcia, J., Mateo, M., Sen,B., Banerjee, M. and von Braun, K. (2011) -- Mapping differential reddening in the inner galactic globular
cluster system. The Astronomical Journal, Volume 141, Issue 5. Draft
Mallik, A, Sen, B., Banerjee, M. and Michailidis, G. (2011) -- Threshold estimation based on a P-value framework. Biometrika, Vol. 98, 4, 887-900.
Tang, R., Banerjee, M. and Michailidis, G. (2011) -- A two-stage hybrid procedure for estimating an inverse regression function. In Annals of Statistics, Vol. 39, 2, 956-989.
Sen, B., Banerjee, M. and Woodroofe, M. B. (2010) -- Inconsistency of bootstrap: the Grenander estimator.
Annals of Statistics, Vol. 38, 4, 1953-1977.
Banerjee, M., Mukherjee, D. and Mishra, S. (2009) -- Semiparametric binary regression models under shape constraints with an application to Indian schooling data. In
Journal of Econometrics, Vol. 149, No. 2, 101-117. Final version +
Sen, B., Banerjee, M.,Woodroofe, M. B., Mateo, M. and Walker, M. (2009) -- Streaming motion in Leo 1. In Annals of Applied Statistics, Vol. 3, 1,
Lan, Y., Banerjee, M. and Michailidis, G. (2009) -- Changepoint estimation under adaptive sampling.
Annals of Statistics, Vol 37, 4, 1752--1791. A Companion technical report.
Minor error: omit the word "largest" on Line 6, Paragraph 2, Page 26.
Ghosh, D., Banerjee, M. and Biswas, P. (2008) -- Inference for constrained tumor size distributions. Draft + Supplementary material. Published in Biometrics.
Pal, J. and Banerjee, M. (2008) --
Estimation of smooth link functions in monotone response
models. Journal of Statistical Planning and Inference, Vol 138, Issue 10, pp 3125--3143.
Banerjee, M. (2008) -- Estimating monotone, unimodal and U--shaped failure rates using asymptotic pivots.
Statistica Sinica , Vol 18, 2, pp. 467--492. Latest version
Banerjee, M. and McKeague I.W. (2007) -- Estimating optimal step--function approximations to instantaneous hazard rates. (
Bernoulli, Volume 13, Number 1, pages 279-299.)
Split point for Cox model
Sen, B. and Banerjee, M. (2007) -- A pseudo--likelihood method for analyzing interval censored data.
(Biometrika, Vol. 94, pages 71--86.) Current version.
Banerjee, M., Biswas, P. and Ghosh, D. (2006) -- A Semiparametric Binary Regression Model Involving
Monotonicity Constraints. ( In Scandinavian Journal of Statistics, Vol 33, 4, pp. 673--697).
Semiparametric Binary Regression
Banerjee, M. and Wellner, J.A. (2005)-- Confidence intervals for current status data.
(In Scandinavian Journal of Statistics , Vol. 32, pages 405--424.)
Current version of the paper
Banerjee, M. and McKeague, I.W. (2007) -- Confidence Sets for Split Points in Decision Trees. Current version of the paper Annals of Statistics, Vol 35, No. 2, pages 543--574.
Banerjee, M. (2007) --
Likelihood based inference for monotone response models Annals of Statistics, Vol 35, No. 3, pages 931--956
A more detailed version of the paper
Banerjee, M. (2005) --
Likelihood Ratio Tests under Local Alternatives in Regular Semiparametric Problems
(In Statistica Sinica , Vol 15, No. 3, pages 635 - 644.)
Banerjee, M. (2005) --
Likelihood Ratio Tests under Local and Fixed Alternatives in Monotone Function Problems
(In Scandinavian Journal of Statistics, Vol 32, pages 507 -- 525)
Banerjee, M. and Richardson,
T. (2003): On a
Dualization of Graphical Gaussian Models: A Correction Note.
(Scandinavian Journal of Statistics, Vol 30, 4, pp. 817--821).
Banerjee, M. and Wellner, J.A. (2005) -- Score Statistics for Current Status Data: Comparisons
with Likelihood Ratio and Wald Statistics. (In The International Journal of
Biostatistics, Vol. 1, No. 1, Article 3)
Banerjee, M. and Wellner J.A. (2001): Likelihood Ratio Tests for
Monotone Functions. Paper in Annals of Statistics,
Vol 29, pages 1699 - 1731.
Discussions, Book Chapters, Conference Proceedings:
Banerjee, M. (2020) – Discussion of "Detecting possibly frequent change- points: Wild Binary Segmentation 2 and steepest-drop model selection", by Piotr Fryzlewicz. Journal of the Korean Statistical Society,
Banerjee, M. (2014) -- Discussion of: `Dynamic Treatment Regimes: technical challenges and applications', by Laber et. al. (2014). Electronic Journal of Statistics Vol 8, 1309–1311.
Banerjee, M. (2012) -- Current Status Data in the 21st Century: Some Interesting Developments. In `Interval-Censored Time-to-Event Data: Methods and Applications'.
Chapman and Hall/CRC Biostatistics Series. Paper
Banerjee, M (2009) -- Discussion of `What's so special about semiparametric models?' by Michael Kosorok. Sankhya, Series A, Vol. 71, Part 2, 354-363. Kosorok's article available at Sankhya Webpage.
Banerjee, M. (2009) -- Inference in exponential family regression models under certain shape constraints. Advances in Multivariate Statistical Methods, Statistical Science and Interdisciplinary Research,
Vol 4, pp 249--272. World Scientific. Shorter version and Longer version.
Richardson, T., Bailer, H. and Banerjee, M. (1999): Specification searches using MAG models. Proceedings, ISI Conference, Helsinki,1999.
Richardson, T., Bailer, H. and Banerjee, M. (1999): Tractable
structure search in the presence of latent variables . In Proceedings
of Artificial Intelligence and Statistics '99 (D. Heckerman and J. Whittaker,
eds.), Morgan Kaufmann, San Francisco, CA, pp.142-151.
Awards and Honors:
- Fellow of ASA.
- Fellow of IMS.
- Young Scientist Award (Theory and Methods) from Indian International Statistical Association, 2011.
- IMS Laha Travel Award, 2002.
- Special COVID-19 Propelling Original Data Science (PODS) Grants Awards, 2020. Joint with Ya'acov Ritov. May 2020 -- December 2020.
- NSF grant DMS-191627 [joint with Yuekai Sun] from September 2019 -- August 2022.
- NSF grant DMS-1712962 [joint with Ya'acov Ritov] from July 2017 -- June 2020.
- NSF grant DMS-1308890 from September 2013 -- August 2016
- Associate Professor Grant (Sokol Faculty Award) from University of Michigan, July 2011 -- June 2013.
- NSA grant H98230-11-1-0166 from Dec 2010 -- Dec 2012.
- NSF grant, DMS-1007751 from 7/1/2010 -- 6/30/2013
- NSF grant, DMS-0705288 from 7/1/2007 -- 6/30/2010.
- University of Michigan, Rackham Graduate School Grant, 2006.
- NSF grant, DMS-0306235 from 6/1/2003 -- 5/31/2007.
- Subha Maity, (joint with Yuekai Sun); thesis in progress.
- Hamid Eftekhari, (joint with Ya'acov Ritov); thesis in progress.
- Debarghya Mukherjee, (joint with Ya'acov Ritov); thesis in progress.
- Mingyuan Gao (2019, joint with George Michailidis); Google.
- Jiahe Lin (2018, joint with George Michailidis); Goldman Sachs.
- Zhiyuan Lu (2019, joint with George Michaildis); thesis in progress.
- Ritabrata Das (2015, joint with Bin Nan); At Bank of America.
- Pramita Bagchi (2015, joint with Stilian Stoev);Faculty at George Mason University.
- Nirupam Chakrabarty (2014, joint with George Michailidis); At Wells Fargo .
- Atul Mallik (2013, joint with Michael Woodroofe);Wells Fargo Securities.
- Runlong Tang (2011, joint with George Michailidis);.
- Bodhisattva Sen (2008, joint with Michael Woodroofe); Professor of Statistics, Columbia University, New York City.
- Yan Lan (2007, joint with George Michailidis); currently At Bank of America.
- Jayanta Kumar Pal (2006, joint with Michael Woodroofe); currently Senior Data Scientist at Zendrive.
Stat 710/711 Special Topics Course on Empirical Processes and Concentration Inequalities.
Stat 610 (Theoretical Statistics -- I) Ph.D level.
Stat 611 (Theoretical Statistics -- 2) Ph.D. level.
Stat 612 (Theoretical Statistics -- III) Advanced Theoretical Statistics for Graduate Students.
Stat 426 (Introduction to Theoretical Statistics) Undergraduate Level.
- Stat 425 Undergraduate Probability.
- Stat 412: Undergraduate Probability and Statistical Inference for Engineering Students.
- Stat 510: Probability, Masters Level.
||University of Michigan
Department of Statistics
275, West Hall,
1085 South University
Ann Arbor, MI 48109
||moulib at umich dot edu