Speaker: Dr Subhadra Dasgupta

Abstract: Cokriging and kriging techniques are widely used for modeling and prediction in Geostatistics. The design of the experiment determines the quality of prediction for these models. This talk focuses on the estimation and design aspects of a bivariate collocated cokriging experiment. For a large class of covariance matrices, a linear dependency criterion is identified, which allows the best linear unbiased estimator of the primary variable in a bivariate collocated cokriging setup to reduce to a univariate kriging estimator. Exact optimal designs for efficient prediction for such simple and ordinary reduced cokriging models are determined by minimizing the maximum and the integrated prediction variance. The work is motivated by designing an optimal water monitoring system for an Indian river. This is joint work with Prof. Siuli Mukhopadhyay and Prof. Jonathan Keith.

Biography: Dr. Subhadra Dasgupta is a postdoc at the Department of Mathematics, Ruhr University Bochum. She received her PhD in Statistics from IITB-Monash Research Academy (a joint PhD program between IIT Bombay and Monash University) and M.Sc. in Mathematics from IIT Bombay. She works in the area of design of experiments, spatial statistics,  and sub-sampling techniques for big data.

WED 24 AUGUST 4PM – 5PM AEST MELBOURNE

ZOOM MEETING ID: 873 1557 5255; PASSWORD: 778635