Functional Data Analysis

By Jane-ling Wang, Jeng-Min Chiou, H. Muller. 2016

4,654 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Food Science, Statistics and Probability

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Citations in the corpus

Learning stable and predictive structures in kinetic systems (2018, methodology): “When using the first derivative of a smoothing spline it has been argued that the penalty term in [10] contains the third rather than the second derivative of y Ramsay and Silverman [2005]. We then regress the estimated derivatives on the data.”
Learning Theory for Distribution Regression (2014, methodology): “Functional data analysis techniques: Finally, we note that the distribution regression task might also be interpreted as a functional data analysis (Ramsay and Silverman, 2002, 2005; Müller, 2005) problem by considering the xi probability measures as functions.”
A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province (2016, positive): “Speciﬁcally, the matrix P T is obtained by direct discretization of the temporal penalty term in (3): see Ramsay and Silverman (2005) for details.”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, methodology): “…Liang and Wu 2008; Wu, Xue, and Kumar 2012), the principal differential analysis (PDA) and its extensions (Ramsay 1996; Heckman and Ramsay 2000; Ramsay and Silverman 2005; Poyton et al. 2006; Ramsay et al. 2007; Varziri et al. 2008; Qi and Zhao 2010), and the Bayesian approaches (Putter et al.…”
Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis (2014, methodology): “The FDA literature contains several methods for obtaining consistent estimators of both the eigenfunctions/eigenvalues and the error variance (e.g. Ramsay & Silverman, 2005; Yao et al., 2005).”
Functional data analysis in the Banach space of continuous functions (2017, methodology): “However, the integral role of smoothness has been discussed at length in Ramsay and Silverman [34] and virtually all functions fit in practice are at least continuous.”
Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm (2019, neutral): “Functional Data Analysis (FDA) has received considerable attention from researchers, [28, 46, 47, 62] and practitioners in many diﬀerent ﬁelds, such as spectrometry, meteorology, [43], client segmentation, [38], speech recognition, [49], or physical, [44, 59], and chemical processes, [6, 8].”
Bootstrap-based testing of equality of mean functions or equality of covariance operators for functional data (2016, methodology): “Functional data are collected in many fields of research; see, e.g., Ramsay & Silverman (2005), Ferraty & Vieu (2006) and Horváth & Kokoszka (2012).”
Mechanistic Hierarchical Gaussian Processes (2014, neutral): “20 and 21 of Ramsay (2006) ).”
On data depth in infinite dimensional spaces (2014, methodology): “We refer to Revuz and Yor (1991) for an exposition on Feller processes that include Brownian motions, geometric Brownian motions and Brownian bridges.”
Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, methodology): “Since the early days of FDA, least squares methods have been applied to linear regression with functional predictors, Ramsay and Dalzell (1991) and Ramsay and Silverman (2005).”
Random forests for functional covariates (2016, methodology): “Many statistical methods have their functional counterparts tailored to the specific properties of such data; see for example the work of Ramsay and Silverman.1 In functional data analysis, the observed data are not simply interpreted as a sequence of individual observations obtained on a grid of me”
Modelling function‐valued stochastic processes, with applications to fertility dynamics (2017, methodology): “A well-established tool of functional data analysis is functional principal component analysis (FPCA) (Ramsay and Silverman, 2005) of the random process X.s, t/, which is based on the Karhunen–Loève expansion X.s, t/=μ.s, t/+ ∞∑ r=1 Zrγr.s, t/, s∈S, t ∈T : .3/ Here {γr : r 1} is an orthonormal…”
Functional linear regression with functional response (2017, methodology): “This model is discussed in the monographs by Ramsay and Silverman (2005), Ferraty and Vieu (2006), and Horvath and Kokoszka (2012).”
Functional additive regression (2015, neutral): “04 06 4v 1 [ m at h. ST ] 1 4 O ct 2 01 5 The Annals of Statistics 2015, Vol. 43, No. 5, 2296–2325 DOI: 10.1214/15-AOS1346 c© Institute of Mathematical Statistics, 2015”
Fast Function to Function Regression (2014, background): “We note however that it is not immediately clear how to achieve a scalable estimator for regression functional responses with functional covariates, nor how to analyze such an estimator’s risk since general functional responses are infinite dimensional.”