3 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
Covariance estimation for derivatives of functional data using an additive penalty in P-splines
Yueyun Zhu, Steven Golovkine, Norma Bargary +1
P-splines provide a flexible and computationally efficient smoothing framework and are commonly used for derivative estimation in functional data. Including an additive penalty ter…
Derivative Estimation of Multivariate Functional Data
Yueyun Zhu, Steven Golovkine, Norma Bargary +1
Existing approaches for derivative estimation are restricted to univariate functional data. We propose two methods to estimate the principal components and scores for the derivativ…
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…
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…