Applied Functional Data Analysis: Methods and Case Studies

By James O. Ramsay, Bernard Walter Silverman. 2002

1,292 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Food Science, Genetics

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Citations in the corpus

Fast Distribution To Real Regression (2013, background): “For a comprehensive survey in functional analysis see [1, 18].”
Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development? (2016, methodology): “More detailed descriptions of this technique have been provided elsewhere (Jackson and Sirois, 2009; Ramsay, 2016; Ramsay and Silverman, 2002; Sirois and Brisson, 2014).”
COVARIATE ADJUSTED FUNCTIONAL PRINCIPAL COMPONENTS ANALYSIS FOR LONGITUDINAL DATA (2010, background): “In addition to Ferraty and Vieu (2006) and Wu and Zhang (2006), the sequence of monographs by Ramsay and Silverman (2002, 2005) provide a tutorial for the methodology and applications of “Functional Data Analysis” (FDA).”
Rainbow Plots, Bagplots, and Boxplots for Functional Data (2010, background): “Such functional data may be age-speciﬁc mortality or fertility rates (Hyndman & Ullah 2007), term-structured yield curves (Kargin & Onatski 2008), spectrometry data (Reiss & Ogden 2007), or one of the many applications described by Ramsay & Silverman (2002).”
Decision Trees for Functional Variables (2006, methodology): “Corroborating the conclusions James and other authors reached by independent analyzes on the same dataset (James and Silverman, 2005; Ramsay and Silverman, 2002), the functional splits we learn show that both variability in the y-direction (shrunken bones) and bending to the right are predictive of ”
Pairwise curve synchronization for functional data (2008, methodology): “Ramsay & Silverman (2002) applied the Procrustes method to these data, using 5 break points and penalty parameter 0.5, and we adopted the same parameter values when implementing the proposed pairwise synchronization method.”
Properties of design-based functional principal components analysis (2008, background): “The books by Ramsay and Silverman (2002, 2005) propose an interesting description of the available procedures dealing with functional observations whereas Ferraty and Vieu (2006) present a completely nonparametric point of view.”
Nonparametric Covariance Function Estimation for Functional and Longitudinal Data (2010, neutral): “(see, e.g., Diggle et al., 2002; Ramsay and Silverman, 2002; 2005; Ferraty and Vieu, 2006).”
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.”
Nonparametric drift estimation for i.i.d. paths of stochastic differential equations (2020, methodology): “This problem is typically part of functional data analysis which is devoted to analysis of samples of in(cid:28)nite dimensional data (see e.g. Ramsay and Silverman, 2007, Wang et al., 2016).”
Cross-validated estimations in the single-functional index model (2008, methodology): “Many other interesting examples can be found in [14,15].”
Presmoothing in Functional Linear Regression (2012, background): “When the temporal or spatial grid is fine enough, the sample can be considered as an observation of a random variable on a certain function space (see examples in Ramsay and Silverman (2002, 2005) or Ferraty and Vieu (2006)).”
Defining probability density for a distribution of random functions (2010, methodology): “There is of course a more general and very large methodology for functional data analysis, accessible via the monographs by Ramsay and Silverman (2002, 2005).”
Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, methodology): “Functional data analysis (FDA) (Ferraty and Vieu, 2006; Ramsay and Silverman, 2002, 2005), is an extension of the classic multi-variate analysis which is particularly oriented to handle observations of functional nature, where each observation represents the dependency of a varying quantity on…”
Statistical inferences for functional data (2007, neutral): “22 07 v1 [ m at h. ST ] 1 6 A ug 2 00 7 The Annals of Statistics 2007, Vol. 35, No. 3, 1052–1079 DOI: 10.1214/009053606000001505 c© Institute of Mathematical Statistics, 2007 STATISTICAL INFERENCES FOR”