Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data

Speaker: Benjamin Fitzpatrick, School of Mathematical Sciences, QUT

Title: Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data

Date: 6 February 2017

Abstract: When making inferences concerning the environment, ground-truthed data will frequently be available as point referenced (geostatistical) observations accompanied by a rich ensemble of potentially relevant remotely sensed and in-situ observations.  Modern soil mapping is one such example characterised by the need to interpolate geostatistical observations from soil cores and the availability of data on large numbers of environmental characteristics for consideration as covariates to aid this interpolation. In this talk I will outline my application of Least Absolute Shrinkage Selection Operator (LASSO) regularized multiple linear regression (MLR) to build models for predicting full cover maps of soil carbon when the number of potential covariates greatly exceeds the number of observations available (the p > n or ultrahigh dimensional scenario). I will outline how I have applied LASSO regularized MLR models to data from multiple (geographic) sites and discuss investigations into treatments of site membership in models and the geographic transferability of models developed. I will also present novel visualisations of the results of ultrahigh dimensional variable selection and briefly outline some related work in ground cover classification from remotely sensed imagery.

Details:

Start Date: 06/02/2017