Sanjay Chaudhuri - Publications


Journal Publications Conference Proceedings Technical Reports Manuscripts In progress PhD. thesis

Journal Publications.

  • Sanjay, Chaudhuri and Mark S. Handcock (2018), A Conditional Empirical Likelihood Based Method for Model Parameter Estimation from Complex Survey Datasets. Statistics and Application, The special J. N. K. Rao felicitation issue, The Society of Statistics, Computer and Application, Vol 16, Issue 1, Pages 245-268.
  • Meng Hwee Victor Ong, Sanjay Chauhduri, and Berwin Turlach (2018), Edge selection for undirected graphs. Journal of Statistical Computation and Simulation, Vol 88, Issue 17, Pages 3291-3322.
  • 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, Pages 293-320.
  • Tatsuya Kubokawa, Shonosuke Sugasawa, Malay Ghosh and Sanjay Chaudhuri (2016), Prediction in heteroscedastic nested error regression models with random dispersions. Statistica Sinica Vol 26, Pages 465-492.
  • Kim Cuc Pham, David J. Nott, Sanjay Chaudhuri (2014), A note on approximating ABC-MCMC using flexible classifiers. STAT , Vol 3, Issue 1, pages 218-227.
  • 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.
  • Antar Bandypadhyay and Sanjay Chaudhuri (2014), Variance estimation for tree order restricted normal models. Statistics, Vol 48, Issue 5, pp 1122-1137.
  • Michael D. Perlman and Sanjay Chaudhuri (2012), Reversing the Stein Effect. Statistical Science Vol 27, No 1, pp 135-143.
  • Sanjay Chaudhuri and Malay Ghosh (2010), Empirical Likelihood for Small Area Estimation. Biometrika, Vol 98(2), 473-480.
  • 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.
  • 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.
  • 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.
  • 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.
  • Sanjay Chaudhuri, Mathias Drton and Thomas S. Richardson (2007), Estimation of a covariance matrix with zeros. Biometrika, Vol 94(1), pp 199-216.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

    Publication in refereed conference proceedings

  • Sanjay Chaudhuri and Thomas Richardson (2003).Using the structure of d-connecting paths as a qualitative measure of strength of dependence., Uncertainty in Artificial Intelligence, 2003, Pg 116.
  • Sanjay Chaudhuri, Mark S. Handcock, Michael S. Rendall (2007). A 2-step Empirical likelihood approach for combining sample and population data in regression estimation , Proceedings of the ISI 2007, published in CD.

    Technical reports.

  • Sanjay Chaudhuri and Mathias Drton (2003). On the Bias and Mean-square Error of the Sample Minimum and the Maximum Likelihood Estimator for two Ordered Normal Means. Technical Report no. 432, Department of Statistics, University of Washington, Seattle.
  • Sanjay Chaudhuri, Mark S. Handcock and Michael S. Rendall, A conditional empirical likelihood approach for combining sampling design and population level information. Technical Report no. 03_2010, Department of Statistics and Applied Probability, National University of Singapore. ----This technical report has since been split and its content has been subsumed in other articles.


    Manuscripts.

  • Jhimli Bhattacharyya, Gopinatha Suresh Kumar, Souvik Maity, Daisuke Miyoshi and Sanjay Chaudhuri, An Unified Statistical Procedure to Analyse Irreversible Thermal Curves.
  • Sanjay Chaudhuri, Subhroshekhar Ghosh, David J. Nott, and Kim Cuc Pham. An easy-to-use empirical likelihood ABC method.
  • Sanjay Chaudhuri Tatsuya Kubokawa and Shonosuke Sugasawa. Use of Covariance Moment Equation for Improving Variance Estimators.
  • Sanjay Chaudhuri and Teng Yin. A two-step Metropolis Hastings method for Bayesian empirical likelihood computation.
  • Sanjay Chaudhuri, Mark S. Handcock and Michael Rendall. Population level information combined parameter estimation from complex survey datasets.
  • Monalisa Char, Amit K Chakraborty, Smarajit Manna, Subhrangsu Aditya, Sanjay Chaudhuri, and Abhijit Kar. Effect of thin film multilayer on the morphologuical and functional properties of lead free solder joint interface.
  • Dang Trung Kien, Neo Han Wai and Sanjay Chaudhuri. elhmc: An R Package for Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood .

    Work in progress (selected).

  • Empirical likelihood based deviance information criterion. [with Teng Yin ]
  • On almost qualitative comparison of signed partial correlation and regression coefficients for a Gaussian Random vector. [with Victor Meng Hwee Ong and Jian Hao Tan ]
  • An empirical likelihood based estimator for respondent driven sampled data. [with Mark Handcock ]
  • Empirical likelihood based Bayesian Methodology for complex survey datasets. [ with Malay Ghosh ]

    PhD Thesis

    Using the structure of d-connecting paths as a qualitative measure of the strength of dependence. (2005), Department of Statistics, University of Washington, Seattle.

    Home Resume Teaching Softwares