15 citations · 34 across the 10 of their papers we have counts for
11 papers
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…
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…
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…
Adaptive estimation of irregular mean and covariance functions
Steven Golovkine, Nicolas Klutchnikoff, Valentin Patilea
Nonparametric estimators for the mean and the covariance functions of functional data are proposed. The setup covers a wide range of practical situations. The random trajectories a…
FDApy: a Python package for functional data
Steven Golovkine
We introduce FDApy, an open-source Python package for the analysis of functional data. The package provides tools for the representation of (multivariate) functional data defined o…
Clustering multivariate functional data using unsupervised binary trees
Steven Golovkine, Nicolas Klutchnikoff, Valentin Patilea
We propose a model-based clustering algorithm for a general class of functional data for which the components could be curves or images. The random functional data realizations cou…
Learning the smoothness of noisy curves with application to online curve estimation
Steven Golovkine, Nicolas Klutchnikoff, Valentin Patilea
Combining information both within and across trajectories, we propose a simple estimator for the local regularity of the trajectories of a stochastic process. Independent trajector…