3 citations · 7 across the 7 of their papers we have counts for
9 papers
Functional Moments Regression
Mingyuan Li, Martin A. Lindquist, Edward Gunning +1
The Gaussian Process (GP) assumption is often used in functional data analysis. We propose a method to assess departures from the GP assumption, both in terms of the shape of the d…
Concurrence: A dependence criterion for time series, applied to biological data
Evangelos Sariyanidi, John D. Herrington, Lisa Yankowitz +8
Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often exhibit complex non-linear interactions…
Is Stephen Curry really a guard? A new perspective on player typologies using functional data analysis
Steven Golovkine, Edward Gunning
We present a novel representation of NBA players' shooting patterns based on Functional Data Analysis (FDA). Each player's charts of made and missed shots are treated as smooth fun…
CLaRe: Compact near-lossless Latent Representations of High-Dimensional Object Data
Emma Zohner, Edward Gunning, Giles Hooker +1
Latent feature representation methods play an important role in the dimension reduction and statistical modeling of high-dimensional complex data objects. However, existing approac…
A Multivariate Multilevel Longitudinal Functional Model for Repeatedly Observed Human Movement Data
Edward Gunning, Steven Golovkine, Andrew J. Simpkin +7
Biomechanics and human movement research often involves measuring multiple kinematic or kinetic variables regularly throughout a movement, yielding data that present as smooth, mul…
Analysing kinematic data from recreational runners using functional data analysis
Edward Gunning, Steven Golovkine, Andrew J. Simpkin +7
We present a multivariate functional mixed effects model for kinematic data from a large number of recreational runners. The runners' sagittal plane hip and knee angles are modelle…
An Understanding of Principal Differential Analysis
Edward Gunning, Giles Hooker
In functional data analysis, replicate observations of a smooth functional process and its derivatives offer a unique opportunity to flexibly estimate continuous-time ordinary diff…
On the estimation of the number of components in multivariate functional principal component analysis
Steven Golovkine, Edward Gunning, Andrew J. Simpkin +1
Happ and Greven (2018) developed a methodology for principal components analysis of multivariate functional data observed on different dimensional domains. Their approach relies on…
On the use of the Gram matrix for multivariate functional principal components analysis
Steven Golovkine, Edward Gunning, Andrew J. Simpkin +1
Dimension reduction is crucial in functional data analysis (FDA). The key tool to reduce the dimension of the data is functional principal component analysis. Existing approaches f…