Clough, Alan R., Evans, Angela, Graham, Veronica, Catterall, Janet, Lakeman, Richard, Gilroy, John, Pratt, Gregory, Petrucci, Joe, Orda, Ulrich, Sehdev, Rajesh, Thornton, Neale, Das, Sourav, Yearsley, Gillian, and Stone, Richard (2023) Emergency examination authorities in Queensland, Australia. Emergency Medicine Australasia. (In Press)

Abdallah, M., Joone, C., Edwards, S., Das, S., and Cavalieri, J. (2022) Comparison of the initial ovarian response, the synchrony of oestrus and ovulation and chronic stress response after administration of 100 or 250 μg of GnRH to randomly cycling Bos indicus cattle. Australian Veterinary Journal, 100 (10). pp. 479-491.

Cavalieri, Jo, and Das, Sourav (2022) Modification of a GnRH-based system to synchronise oestrus in Bos indicus cattle improves pregnancy rates to AI in heifers but not cows. Australian Veterinary Journal, 100 (4). pp. 162-171.

Das, Sourav, Sheth, Arshay Nimish, Bansal, Priya, Chuah, Joon, and Wasson, Robert (2022) A statistical comparison of flood-related economic damage in Indian states with reflections on policy implications. International Journal of Disaster Risk Reduction, 72. 102835.

Wibawa, Pierre, Matta, Gabriel, Das, Sourav, Eratne, Dhamidu, Farrand, Sarah, Desmond, Patricia, Velakoulis, Dennis, and Gaillard, Frank (2021) Bringing psychiatrists into the picture: automated measurement of regional MRI brain volume in patients with suspected dementia. Australian and New Zealand Journal of Psychiatry, 55 (8). pp. 799-808.

Sharp, C., Schepen, A., Das, S., and Everingham, Y. (2021) Geostatistical merging of weather radar data with a sparse rain gauge network in Queensland. In: Proceedings of the 24th International Congress on Modelling and Simulation. pp. 644-650. From: MODSIM 2021: 24th International Congress on Modelling and Simulation, 5-10 December 2021, Sydney, NSW, Australia.

Priyadarshana, Anuradha, and Das, Sourav (2021) Machine based algorithm for characterizing and precursory monitoring of landslides. In: [Presented at the 7th International Conference on Time Series and Forecasting]. From: ITISE 2021: 7th International Conference on Time Series and Forecasting, 19-21 July 2021, Gran Canaria, Spain and Online.

Das, Sourav, Subba Rao, Suhasini, and Yang, Junho (2021) Spectral methods for small sample time series: a complete periodogram approach. Journal of Time Series Analysis, 42 (5-6). pp. 597-621.

Das, Sourav, Subba Rao, Tata, and Boshnakov, Georgi N. (2020) An asymptotically unbiased weighted least squares estimation criterion for parametric variograms of second order stationary geostatistical processes. Communications in Statistics: simulation and computation, 49 (7). pp. 1839-1854.

Das, Sourav, and Tordesillas, Antoinette (2019) Near real-time characterization of spatio-temporal precursory evolution of a rockslide from radar data: integrating statistical and machine learning with dynamics of granular failure. Remote Sensing, 11 (23). 2777.

Wallbridge, Peter, Parry, Selina M., Das, Sourav, Law, Candice, Hammerschlag, Gary, Irving, Louis, Hew, Mark, and Steinfort, Daniel (2018) Parasternal intercostal muscle ultrasound in chronic obstructive pulmonary disease correlates with spirometric severity. Scientific Reports, 8. 15274.

Das, Sourav, Cook, Alex R., Wah, Win, Win, Khin Mar Kyi, Chee, Cynthia Bin Eng, Wang, Yee Tang, and Hsu, Li Yang (2017) Spatial dynamics of TB within a highly urbanised Asian metropolis using point patterns. Scientific Reports, 7. 36.

Das, Sourav, and Nason, Guy P. (2016) Measuring the degree of non-stationarity of a time series. Stat, 5 (1). pp. 295-305.

Wah, Win, Das, Sourav, Earnest, Arul, Lim, Leo Kang Yang, Chee, Cynthia Bin Eng, Cook, Alex Richard, Wang, Yee Tang, Win, Khin Mar Kyi, Ong, Marcus Eng Hock, and Hsu, Li Yang (2014) Time series analysis of demographic and temporal trends of tuberculosis in Singapore. BMC Public Health, 14 (1). 1121.

Rao, Tata Subba, Das, Sourav, and Boshnakov, Georgi N. (2014) A frequency domain approach for the estimation of parameters of spatio-temporal stationary random processes. Journal of Time Series Analysis, 35 (4). pp. 357-377.