Functional Data Analysis

By James O. Ramsay. 2005

400 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b2e56a936b9dd333d6b81cab0fab8993589bb0c1

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Citations in the corpus

Prediction of functional ARMA processes with an application to traffic data (2016, background): “ of discrete to functional data. For theoretical considerations we work with data in functional form. For a sound introduction on the transformation from vector observations to functions, we refer to [15]. We want to assess temporal dependence between diﬀerent days; i.e., our goal is a realistic tim”
Tests for Error Correlation in the Functional Linear Model (2010, background): “g in Ramsay and Silverman (2005) and Yao et al. (2005b), so a detailed exposition is necessary.”
Regularized linear and kernel redundancy analysis (2007, methodology): “This generalized form of ridge regression is useful for incorporating more complicated forms of regularization such as smoothness (Ramsay and Silverman, 2006).”
Tensor Regression with Applications in Neuroimaging Data Analysis (2012, methodology): “ predictor. Fitting such models commonly involves representing functions as a linear combination of basis functions, which are either pre-specied or obtained from principal component decompositions (Ramsay and Silverman, 2005). Reiss and Ogden (2010) notably extended this idea to a functional regres”
Testing stationarity of functional time series (2014, methodology): “To select d, we use the usual ''cumulative variance'' approach recommended by Ramsay and Silverman (2005) and Horváth and Kokoszka (2012); d is chosen so that roughly v% of the sample variance is explained by the first d principal components.”
Spatial spline regression models (2013, unknown): “Furthermore, the value of the smoothing parameter (cid:21) may be selected by Generalized-Cross-Validation (see, e.g., Ramsay and Silverman 2005, and references therein): Finally, the value predicted for a new observation, at point p n +1 and with covariates w n +1 , is given by whose mean and…”
MCMC Methods for Functions: ModifyingOld Algorithms to Make Them Faster (2012, background): “an the dimension du of the object uto be estimated. A frequently occurring situation of this type is where the data yis a ﬁxed ﬁnite-dimensional set of observations, whilst the unknown is a function u[47]. If the function is modelled non-parametrically then the ratio dy/du can hence be arbitrarily s”
Multivariate Functional Halfspace Depth (2014, methodology): “A popular example is the bivariate gait data set, which contains the simultaneous variation of the hip and knee angles for 39 children at 20 equally spaced time points (Ramsay and Silverman, 2006).”
Pointwise testing with functional data using the Westfall–Young randomization method (2008, methodology): “The methods of functional data analysis (Ramsay and Silverman, 2005) are designed to deal with these issues.”