Research & Publications

Ph.D. thesis

Spatiotemporal Modeling with Biomedical and Environmental Applications. Link-here.

Papers in preparation

• Mukhopadhyay, M. and Hazra, A. (2024+). A graph-based distribution-free Pettitt test for spatiotemporal data.

• Hazra, A., Alahmadi, E., and Huser, R. (2024+). Extreme-Value Analysis.

• Hazra, A., Reich, B. J., Shaby, B. and Staicu, A. (2024+). A semiparametric spatiotemporal Bayesian model for bulk and extremes of the Fosberg Fire Weather Index. Link- https://arxiv.org/abs/1812.11699.

• Dey, A. and Hazra, A. (2024+).  A Semiparametric Generalized Exponential Regression Model with a Principled Distance-based Prior for Analyzing Trends in Rainfall. Link- https://arxiv.org/abs/2309.03165.

Submitted papers

• Pakrashi, A., Hazra, A., Raveendran, S. M., and Balakrishnan, K. (2024+). Approximate Bayesian inference for high-resolution spatial disaggregation using alternative data sources. Under Review in The Journal of Agricultural, Biological and Environmental Statistics (JABES). Link- https://arxiv.org/abs/2407.11173.

• Kumar, S., Srivastava, A. K., Hazra, A., and Ojha, R. Temporal variability in soil hydraulic properties in a cropped agricultural plot within the Ganga Basin, India. Under Review in Catena.

• Sahoo, I., Majumder, S., Hazra, A., Rappold, A. G., and Bandyopadhyay, D. (2024+). Computationally Scalable Bayesian SPDE Modeling for Censored Spatial Responses. Under Revision in New England Journal of Statistics in Data Science. Link- https://arxiv.org/abs/2403.15670.

Published/Accepted papers/book chapters (Ph.D. and post-Ph.D.)

• Hazra, A., Huser, R., and Bolin, D. (2024). Realistic and Fast Modeling of Spatial Extremes over Large Geographical Domains. Just accepted at the Journal of Computational and Graphical Statistics (JCGS). Link- https://arxiv.org/abs/2112.10248.

• Belzile, L. R., Hazra, A., and Yadav, R. (2024). A utopic adventure in the modeling of conditional univariate and multivariate extremes. Extremes. Link- https://arxiv.org/abs/2312.13517.

• Shao, X., Hazra, A., Richards, J., and Huser, R. (2024).  Flexible Modeling of Non-Stationary Extremal Dependence Using Spatially-Fused LASSO and Ridge Penalties. Technometrics. Link-https://doi.org/10.1080/00401706.2024.2388549.

• Hazra, A.  and Bose, S. (2024).  Estimating Changepoints in Extremal Dependence, Applied to Aviation Stock Prices During COVID-19 Pandemic. Journal of Applied Statistics (JAS). Link:  https://www.tandfonline.com/doi/full/10.1080/02664763.2024.2373939

• Hazra, A. and Ghosh, A. (2024). Robust statistical modeling of monthly rainfall: The minimum density power divergence approach. Sankhya: The Indian Journal of Statistics, Series B. Link-https://link.springer.com/article/10.1007/s13571-024-00324-0.

• Hazra, A. (2024). Minimum Density Power Divergence Estimation for the Generalized Exponential Distribution. Communications in Statistics – Theory and Methods. Link- https://www.tandfonline.com/doi/full/10.1080/03610926.2024.2329768.

• Hazra, A., Nag, P., Yadav, R., and Sun, Y. (2024). Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study. Just accepted. The Journal of Agricultural, Biological and Environmental Statistics (JABES). Link- https://link.springer.com/article/10.1007/s13253-024-00602-4

• Cisneros, D., Hazra, A., and Huser, R. (2024).  Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model. Just accepted. The Journal of Agricultural, Biological and Environmental Statistics (JABES). Link- https://link.springer.com/article/10.1007/s13253-023-00596-5

• Cisneros, D., Gong, Y., Yadav, R., Hazra, A., and Huser, R. (2023).  A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes. Extremes. Link- https://link.springer.com/article/10.1007/s10687-022-00460-8

• Birgand, F., Chapman, K., Hazra, A., Gilmore, T., Brown, A., Etheridge, R., and Staicu, A. (2022). Field performance of the GaugeCam image-based water level measurement system. PLOS Water.

• Hazra, A., Huser, R., and Johannesson, A. V., (2022). Bayesian Latent Gaussian Models for High-Dimensional Spatial Extremes. Book chapter in Statistical Modeling Using Bayesian Latent Gaussian Models - With Applications in Geophysics and Environmental Sciences. Springer.

• Sahoo, I., and Hazra, A., (2021). Contamination mapping in Bangladesh using a multivariate spatial Bayesian model for left-censored data. Journal of the Indian Statistical Association. Link- https://arxiv.org/abs/2106.15730.

• Hazra, A. and Huser, R. (2020). Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model. The Annals of Applied Statistics. Vol. 15, Issue 2. Link- https://projecteuclid.org/journals/annals-of-applied-statistics/volume-15/issue-2.

• Hazra, A., Reich, B. J., and Staicu, A. (2020). A multivariate spatial skew-$t$ process for joint modeling of extreme precipitation indexes. Environmetrics. Vol. 31, Issue 3.  Link- https://onlinelibrary.wiley.com/doi/full/10.1002/env.2602.

• Hazra, A., Reich, B. J., Reich, D. S., Shinohara, R. T., and Staicu, A. (2019). A spatiotemporal model for longitudinal image-on-image regression. Statistics in Biosciences. Vol. 11, Issue 1, pp 22-46. Link- https://link.springer.com/article/10.1007/s12561-017-9206-z.

Published papers (pre-Ph.D.)

• Hazra, A., Bhattacharya, S., and Banik, P. (2018). A Bayesian zero-inflated exponential distribution model for the analysis of weekly rainfall of the eastern plateau region of India. MAUSAM. Vol. 69, Issue 1, pp 19-28. Link- https://metnet.imd.gov.in/imdmausam/.

• Hazra, A., Bhattacharya, S., Banik, P., and Bhattacharya, S. (2017). A note on the misuses of the variance test in meteorological studies. Meteorology and Atmospheric Physics. Vol. 129, Issue 6. pp 645-658. Link- https://link.springer.com/article/10.1007/s00703-016-0490-9.

• Das, M., Hazra, A., Sarkar, A., Bhattacharya, S., and Banik, P. (2017). Comparison of spatial interpolation methods for estimation of weekly rainfall in West Bengal, India. MAUSAM. Vol. 68, Issue 1, pp 41-50. Link- https://metnet.imd.gov.in/imdmausam/.

• Saha, E., Hazra, A., and Banik, P. (2016). SARIMA modeling of the monthly average maximum and minimum temperatures in the eastern plateau region of India. MAUSAM. Vol. 67, Issue 4, pp 841-848. Link- https://metnet.imd.gov.in/imdmausam/.

• Mukhopadhyay, S., Hazra, A., Bhowmick, A. R. and Bhattacharya, S. (2016). On comparison of relative growth rates under different environmental conditions with application to biological data. METRON. Vol. 74, Issue 3. pp 311-337. Link- https://link.springer.com/article/10.1007/s40300-016-0102-y.

• Hazra, A. (2015). A simulation-free exact conditional goodness-of-fit test for the binomial distribution. Calcutta Statistical Association Bulletin. Vol. 67, Issue 3-4. pp 105-128. Link- https://journals.sagepub.com/doi/abs/10.1177/0008068320150302.

• Hazra, A., Bhattacharya, S. and Banik, P. (2014). Modeling Nakshatra-wise rainfall data of the eastern plateau region of India. MAUSAM. Vol. 65, Issue 2. pp 264--270. Link- https://metnet.imd.gov.in/imdmausam/.

• Hazra, A. (2013). An exact Kolmogorov-Smirnov test for the negative binomial distribution with an unknown probability of success. Research and Reviews: Journal of Statistics. Vol. 2, Issue 1. pp 1--13. Link- http://sciencejournals.stmjournals.in/index.php/RRJoST/article/view/2594.

Some unpublished manuscripts

• Hazra, A. (2017). Analysis of annual frequencies of cyclones over the Bay of Bengal: before and after 2004 Indian Ocean tsunami. Link- https://arxiv.org/abs/1708.03972.

• Hazra, A. (2015). A time-varying parameter-based seasonally-adjusted Bayesian state-space model for forecasting. Link- https://arxiv.org/abs/1512.02149.

Research projects

• Forecast calibration of PM2.5 using Bayesian model averaging- with Brian Reich, Department of Statistics, North Carolina State University and Ana Rappold, United States Environmental Protection Agency.

• A nonparametric Bayesian state-space model for the analysis of annual frequencies of the North Indian Ocean cyclones- with Sourabh Bhattacharya, ISRU, ISI Kolkata.

• A Hierarchical Bayesian Multivariate GEV Model for the analysis of Extreme River Inflow at Five Reservoirs on the River Damodar, India- with Elizabeth Mannshardt, Department of Statistics, North Carolina State University.

• A basis function-based non-parametric non-stationary state-space model for spatiotemporal data- with Sourabh Bhattacharya, ISRU, ISI Kolkata.

• Spatial statistical analysis of tropical rainfall in Himalayan and sub-Himalayan regions and Damodar valley based on TRMM Data - with Prof. Parthasarathi Ghosh, GSU, ISI Kolkata.