Geospatial analysis of multivariate processes applied to soil pollution data

May 22, 2024
3 p.m. PDT

Majumdar’s work studies geospatial processes, especially the covariance structure of multivariate geospatial variables that cannot be explained or modeled using usual stationary structures. It extends parametric methods to a parsimonious semiparametric model using a kernel convolution technique. This work has been applied to soil pollution data in a heterogeneous ecology in Phoenix, Arizona. Geospatial statistics and analysis have been widely applied in biostatistics and genomics, and to study outbreaks of disease in epidemiological studies.

Email [email protected] for the Zoom link.

Event Type: 
Biostatistics and Bioinformatics Seminar