8 citations · 9 across the 6 of their papers we have counts for
9 papers
Order selection with confidence for finite mixture models
Hien D Nguyen, Daniel Fryer, Geoffrey McLachlan
The determination of the number of mixture components (the order) of a finite mixture model has been an enduring problem in statistical inference. We prove that the closed testing…
Shapley values for feature selection: The good, the bad, and the axioms
Daniel Fryer, Inga Strümke, Hien Nguyen
The Shapley value has become popular in the Explainable AI (XAI) literature, thanks, to a large extent, to a solid theoretical foundation, including four "favourable and fair" axio…
SARGDV: Efficient identification of groundwater-dependent vegetation using synthetic aperture radar
Mason Terrett, Daniel Fryer, Tanya Doody +2
Groundwater depletion impacts the sustainability of numerous groundwater-dependent vegetation (GDV) globally, placing significant stress on their capacity to provide environmental…
-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences
Daniel Fryer, Hien Nguyen, Pascal Castellazzi
We state theoretical properties for -means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space, that provides a natural and favourable representat…
A binary-response regression model based on support vector machines
Hien D Nguyen, Daniel V Fryer
The soft-margin support vector machine (SVM) is a ubiquitous tool for prediction of binary-response data. However, the SVM is characterized entirely via a numerical optimization pr…
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies
Daniel Vidali Fryer, Inga Strümke, Hien Nguyen
Shapley values have become increasingly popular in the machine learning literature thanks to their attractive axiomatisation, flexibility, and uniqueness in satisfying certain noti…
Shapley value confidence intervals for attributing variance explained
Daniel Fryer, Inga Strumke, Hien Nguyen
The coefficient of determination, the , is often used to measure the variance explained by an affine combination of multiple explanatory covariates. An attribution of this exp…
Spherical data handling and analysis with R package rcosmo
Daniel Fryer, Andriy Olenko
The R package rcosmo was developed for handling and analysing Hierarchical Equal Area isoLatitude Pixelation(HEALPix) and Cosmic Microwave Background(CMB) radiation data. It has mo…
rcosmo: R Package for Analysis of Spherical, HEALPix and Cosmological Data
Daniel Fryer, Ming Li, Andriy Olenko
The analysis of spatial observations on a sphere is important in areas such as geosciences, physics and embryo research, just to name a few. The purpose of the package rcosmo is to…