Photo of Soutir Bandyopadhyay

Soutir Bandyopadhyay

  • Applied Mathematics and Statistics
  • Colorado School of Mines
    Golden, CO 80401
    USA

Education

  • Ph.D., Statistics - 2010, Department of Statistics, Texas A&M University under the supervision of Dr. Soumendra N. Lahiri.
  • M. Stat., Statistics - 2005, Indian Statistical Institute, New Delhi, India. (Specialization: Advanced Probability)
  • B. Sc., Statistics - 2003, St. Xavier's College, Kolkata, India.

Research

  • Spatial and Environmental Statistics
  • Time Series
  • Bioinformatics
  • Bootstrap/Resampling methods
  • Large Sample Theory.

Publications

  • On the Nonstandard Distribution of Empirical Likelihood Estimators with Spatial Data. (with M. Van Hala et al.), Journal of Statistical Planning and Inference, (2017).
  • Asymptotic Theory for Varying Coefficient Regression Models with Dependent Data (with A. Maity), Annals of the Institute of Statistical Mathematics (2017).
  • A Test for Stationarity for Spatio-temporial Data. (with C. Jentsch, and S. Subba Rao), Journal of Time Series Analysis (2016).
  • A Test for Stationarity for Irregularly Spaced Spatial Data (with S. Subba Rao), Journal of the Royal Statistical Society, Series B. (2016).
  • A Frequency Domain Empirical Likelihood Method for Irregularly Spaced Spatial Data (with S. N. Lahiri and D. J. Nordman), Annals of Statistics (2015).
  • A Multi-resolution Gaussian Process Model for the Analysis of Large Spatial Data Sets (with D. W. Nychka et al.), Journal of Computational and Graphical Statistics (2015).
  • A Note on Efficient Density Estimators of Convolutions. Journal of Statistical Planning and Inference (2012).
  • Analysis of Sabine River Flow Data Using Semiparametric Spline Modeling.(with A. Maity), Journal of Hydrology, (2011).
  • Asymptotic Properties of Discrete Fourier Transforms for Spatial Data. (with S. N. Lahiri), Sankhya, Series A, (2009).
  • Resampling-based Bias-corrected Time Series Prediction.(with S. N. Lahiri), Journal of Statistical Planning and Inference, (2009).