Functional Data Analysis With R And Matlab

By M. Frei. 2016

384 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Tests for separability in nonparametric covariance operators of random surfaces (2015, methodology): “To do this, we rely on a strategy from Functional Data Analysis (Ramsay & Silverman 2002, 2005, Ferraty & Vieu 2006, Ramsay et al. 2009, Horváth & Kokoszka 2012b), which consists in projecting the observations onto a carefully chosen low-dimensional subspace.”
Adaptive Bayesian Procedures Using Random Series Priors (2014, background): “The medfly data [Ramsay et al., 2009] records the number of eggs laid by 50 fruit flies on each of 26 days.”
Global envelope tests for spatial processes (2013, methodology): “…is closely connected to the methods that are used in functional data analysis, where measures of functional depth are typically used to describe the centrality of a curve among a set of curves (e.g. Ramsay and Silverman (2006), Ramsay et al. (2009) and López-Pintado and Romo (2009, 2011)).”
A general framework for functional regression modelling (2017, methodology): “Software implementations of this approach are available for R (package fda, Ramsay et al., 2014) and MATLAB (Ramsay et al., 2009).”
Functional Data Analysis (2016, background): “…can explore the various aspects of this ﬁeld through several monographs (Bosq 2000; Ramsay & Silverman 2005; Ferraty & Vieu 2006; Wu & Zhang 2006; Ramsay, Hooker & Graves 2009; Horvath & Kokoszka 2012; Hsing & Eubank 2015) and review articles (Rice 2004; Zhao, Marron & Wells 2004; M¨uller 2005,…”