Research Interests

Theory and application of empirical likelihood, analysis of complex survey data, statistical data integration, application of statistics in demography, statistics on network data, Bayesian empirical likelihood, graphical Markov models, covariance estimation for high-dimensional data, graphical model selection, causality, approximate Bayesian computation, Markov chain Monte Carlo techniques, order restricted inference, small area estimation, mixed effects models, application of statistics to real-life problems; Statistical analysis of data obtained from experiments in natural, engineering, and marketing sciences, machine learning, artificial intelligence.

Publications

1. Shubhroshekhar Ghosh, Sanjay Chaudhuri, and Ujan Gangopadhyay (2023). Maximum likelihood estimation under constraints: Singularities and random critical points. IEEE Transactions on Information Theory, Vol 69(12), 7976–7997.
2. Sanjay, Chaudhuri, Tatsuya, Kubokawa and Shonosuke, Sugasawa. (2022) Covariance based Moment Equations for Improved Variance Component Estimation. Statistics, Vol 56(6), pp 1290-1318.
3. Satarupa Bhattacharjee, Shuting Liao, Debashis Paul, and Sanjay Chaudhuri. (2022) Taming the pandemic by doing the mundane. Ghosh, Aurobindo, Haldar, Amit, and Bahumik, Kalyan (Eds.), Managing Complexity and Covid-19: Life, Liberty or the Pursuit of Happiness. Routledge.pp 62-82
4. Satarupa Bhattacharjee, Shuting Liao, Debashis Paul, and Sanjay Chaudhuri. (2022) Inference on the dynamics of COVID-19 in the United States. Scientific Reports, Vol 12(1):2253.
5. Tatsuya Kubokawa, Shonosuke Sugasawa, Hiromasa Tamae and Sanjay Chaudhuri. (2021) General Unbiased Estimating Equations for Variance Components in Linear Mixed Models. Japanese Journal of Statistics and Data Science, Vol 4, pp 841–859.
6. Sanjay Chaudhuri, and Mark S. Handcock (2018) A Conditional Empirical Likelihood Based Method for Model Parameter Estimation from Complex Survey Datasets. The special J. N. K. Rao felicitation issue, The Society of Statistics, Computer and Application, Vol 16, No 1, pp 245–268.
7. Meng Hwee Victor Ong, Sanjay Chaudhuri, and Berwin Turlach. (2018) Edge selection for undirected graphs. Journal of Statistical Computation and Simulation, Vol 88, No 17, pp 3291–3322.
8. Sanjay Chaudhuri, Debashis Mondal, and Teng Yin (2017). Hamiltonian Monte Carlo in Bayesian Empirical likelihood computation. Journal of the Royal Statistical Society Series B, Vol 79, Issue 1, pp 293-320.
9. Tatsuya Kubokawa, Shonosuke Sugasawa, Malay Ghosh, and Sanjay Chaudhuri (2016). Prediction in heteroscedastic nested error regression models with random dispersions. Statistica Sinica, Vol 26, pp 465-492.
10. Kim Cuc Pham, David J. Nott, Sanjay Chaudhuri (2014), A note on approximating ABC-MCMC using flexible classifiers. STAT, Vol 3, Issue 1, pp 218-227.
11. Sanjay Chaudhuri (2014). Qualitative inequalities for squared partial correlations of a Gaussian random vector. Annals of Institute of Statistical Mathematics, Vol 66, No 2, pp 345-367.
12. Antar Bandypadhyay and Sanjay Chaudhuri (2014). Variance estimation for tree order restricted normal models. Statistics, Vol 48, Issue 5, pp 1122-1137.
13. Michael D. Perlman and Sanjay Chaudhuri (2012). Reversing the Stein Effect. Statistical Science, Vol 27, No 1, pp 135-143.
14. Sanjay Chaudhuri and Malay Ghosh (2010). Empirical Likelihood for Small Area Estimation. Biometrika, Vol 98(2), pp 473-480.
15. Sanjay Chaudhuri and Gui Liu Tan (2010). On qualitative comparison of partial regression coefficients for Gaussian graphical Markov models. in Algebraic Methods in Statistics and Probability II, Contemporary Mathematics, 519, Vianna, Marlos A. G. and Wynn, Henry P., editors, pp 125-133.
16. Sanjay Chaudhuri, Mark S. Handcock, and Michael Rendall (2008). Generalised linear models incorporating population level information: An empirical likelihood based approach. Journal of the Royal Statistical Society Series B, Vol 70, Part 2, pp 311-328.
17. Abhijit Kar, Sanjay Chaudhuri, Pratik K. Sen, and Ajoy Kumar Ray (2007). Evaluation of hardness of the interfacial reaction products at the alumina-stainless steel brazed interface by modeling of nanoindentation results. Scripta Materalia, Vol 57, pp 881-884.
18. Sanjay Chaudhuri and Michael D. Perlman (2007) Consistent estimation of the minimum normal mean under the tree-order restriction. Journal of statistical planning and inference, Vol 137, pp 3317 – 3335.
19. Sanjay Chaudhuri, Mathias Drton, and Thomas S. Richardson (2007). Estimation of a covariance matrix with zeros. Biometrika, Vol 94(1), pp 199-216.
20. Sanjay Chaudhuri, Mark S. Handcock, and Michael S. Rendall (2007). A 2-step empirical likelihood based approach for combining sample and population data in regression estimation. Proceedings of the ISI 2007.
21. Sanjay Chaudhuri and Michael D. Perlman (2006) Two Step-down Tests for Equality of Covariance Matrices. Linear algebra and its applications, vol 417, pp 42-63.
22. Sanjay Chaudhuri and Michael D. Perlman (2005). Biases of the maximum likelihood and Cohen-Sackrowitz estimators for the tree-order model. Statistics & Probability Letters, vol 71, pp 267-276.
23. Sanjay Chaudhuri and Michael D. Perlman (2005). On the Bias and Mean-squared Error of Order-restricted Maximum Likelihood Estimators. Journal of statistical planning and inference, vol 130, pp 229-250.
24. Michael D. Perlman and Sanjay Chaudhuri (2004).The Role of Reversals in Order-restricted Inference. The Canadian Journal of Statistics, Vol 32, No 2, pp 193-198.
25. Sanjay Chaudhuri and Thomas Richardson (2003). Using the structure of d-connecting paths as a qualitative measure of strength of dependence. 19th conference of Uncertainty in Artificial Intelligence. 116–123.
26. Krishna Kumar, Sanjay Chaudhuri, and Alaka Das (2002).Quasiperiodic waves at the onset of zero-Prandtl-number convection with rotation. Physical Review E, Volume 65, 2002.

Technical Reports

1. Sanjay Chaudhuri, Mark S. Handcock, and Michael S. Rendall (2010). A Conditional Empirical Likelihood Approach to Combine Sampling Design and Population Level Information. Tech. rep. 03_2010. Department of Statistics and Applied Probability.
2. Sanjay Chaudhuri, and Mathias Drton (2003). On the Bias and Mean-squared Error of the Sample Minimum and the Maximum Likelihood Estimator for two Ordered Normal Means. Tech. rep. 432. Department of Statistics, University of Washington.

PhD Thesis

1. Sanjay Chaudhuri (2005). Using the structure of d-connecting paths as a qualitative measure of the strength of dependence. PhD thesis. University of Washington.

Pre-prints - Articles in Revision

1. Akito I. Sema, Sanjay Chaudhuri, and Jhimli Bhattacharyya. A brief investigation of the iron content in the ground waters of Dimapur district in Nagaland, India.
2. Sanjay Chaudhuri, Subhroshekhar, Ghosh, David J. Nott, and Kim Cuc Pham. On a Variational Approximation based Empirical Likelihood ABC Method. arXiv: 2011.07721.
3. Jhimli Bhattacharyya, Gopinatha Suresh Kumar, Souvik Maiti, Daisuke, Miyoshi, and Sanjay Chaudhuri. An Unified Statistical Procedure to Analyse Irreversible Thermal Curves. arXiv: 2209.03957.
4. Sanjay Chaudhuri, Mark S. Handcock, and Michael S. Rendall. Population level information combined parameter estimation from complex survey datasets. arXiv:2209.01247.
5. Sanjay Chaudhuri, and Yin Teng. A Two-step Metropolis Hastings Method for Bayesian Empirical Likelihood Computation with Application to Bayesian Model Selection. arXiv: 2209.01269.
6. Dang Trung Kien, Neo Han Wai, and Sanjay Chaudhuri. elhmc: An R Package for Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood. arXiv: 2209.01289.

Teaching

Topics taught:

• Advanced Topics in Applied Statistics.
• Statistical Models: Theory/Applications.
• Applied Regression Analysis.
• Design & Analysis of Experiments.
• Graphical Markov Models.
• Introduction to Data Science.
• Linear Models.
• Sample Surveys.
• Probability.
• Time Series Analysis.
• Demography.
• Freshmen Seminar

Software

1. glmc: A package for combining sample and population information using empirical likelihood. The library is available at https://cran.r-project.org/web/packages/glmc/index.html.
2. ES: Implementation of the Edge Selection Algorithm. The package was mostly coded by Victor Meng Hwee Ong with inputs from Berwin Turlach and me. The library is available at https://cran.r-project.org/web/packages/ES/index.html.
3. elhmc: Sampling from an empirical likelihood Bayesian posterior of parameters using Hamiltonian Monte Carlo. The package was mostly coded by Dang Trung Kien from an initial code written by Neo Han Wai. The library is available at https://cran.r-project.org/web/packages/elhmc/index.html.
4. Anhysnuc: Analysis of Hysteric (Irreversible) Thermal Curves from Nucleic Acid Hybridisation Experiments. This web-based R package analyzes irreversible thermal curves frequently obtained from nucleic acid hybridization experiments. The package in its current form is mostly written by me and Dang Trung Kien. The library is available at https://sanjaychaudhuri.shinyapps.io/anhysnuc/.