- BSc (Hons) Marine Biology & Zoology, UWA.
- Applied Statistics
- Variable Selection
- Shrinkage Methods (e.g. LASSO, Elastic Net, Bayesian LASSO)
CRCSI Biomass Business (4.12): PhD Project 3.1 – Subset selection and spatial inference for soil carbon.
My research focuses on quantifying soil carbon stocks at an individual paddock scale. I am exploring methods to model small numbers of geostatistical observations of soil carbon with broad collections of environmental data all available at much finer spatial resolutions than the soil carbon data. The aim of this modelling is to use these high resolution environmental data to assist the interpolation of the soil carbon observations to full cover maps. I am also interested in visualisation methods to communicate predictions from models, the uncertainty associated with these predictions and the mechanics of the models producing these predictions.
Fitzpatrick, B. R., Lamb, D. W., & Mengersen, K. (2016). Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study . PLoS ONE 11(9): e0162489.
Langlois, T. J., Fitzpatrick, B. R., Fairclough, D. V., Wakefield, C. B., Hesp, S. A., McLean, D. L., Harvey, E. S., Meeuwig, Jessica J. (2012). Similarities between Line Fishing and Baited Stereo-Video Estimations of Length-Frequency: Novel Application of Kernel Density Estimates. (D. M. Bailey, Ed.) PLoS ONE, 7(11).
Group R Tips Blog: One weiRd tip
Thesis in 3 talk at the CRCSI 2014 Conference.