Curves discrimination: a nonparametric functional approach

By Frédéric Ferraty, Philippe Vieu. Computational Statistics & Data Analysis, 2003

323 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8a6cb2536e09cc6683a4c91cf133e3078bd9bdea

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Citations in the corpus

Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models (2006, background): “For instance Ferraty and Vieu [16] are studying regression estimation when the explanatory variable is functional and the response is scalar, and their study is extended to nonstandard regression problems such as time series prediction (see [11]) or curves discrimination (see [ 14 ]).”
Continuous Time-Varying Kriging for Spatial Prediction of Functional Data: An Environmental Application (2010, methodology): “Examples of such methods include exploratory analysis (Ramsay and Silverman, 2005), analysis of variance (Cuevas et al., 2004; Delicado, 2007), regression (Cardot et al., 1999, 2007), non-parametric methods (Ferraty and Vieu, 2006) or multivariate techniques (Ferraty and Vieu, 2003).”
Assessing the finite dimensionality of functional data (2006, background): “See particularly Ferraty and Vieu (2003, 2004), who discussed the importance of semimetrics in this setting.”
Functional Data Analysis (2016, methodology): “…a functional data-analytic approach to signal discrimination, using the FPCA method for dimension reduction (Hall, Poskitt & Presnell 2001) and kernel functional classiﬁcation rules for nonparametric curve discrimination (Ferraty & Vieu 2003; Chang, Chen & Ogden 2014; Zhu, Brown & Morris 2012).”
Model-based clustering for multivariate functional data (2013, background): “A sample of n = 50 curves are simulated according to the following model inspired by Ferraty and Vieu (2003) and Preda (2007): for t ∈ [1, 21], Class 1 : X1(t) = −5 + t/2 + U2h3(t) + U3h2(t) + √ 0.1ǫ(t), X2(t) = −5 + t/2 + U1h1(t) + U2h2(t) + U3h3(t) + √ 0.5ǫ(t), Class 2 : X1(t) = U3h2(t) + √…”
A statistical method for estimating predictable differences between daily traffic flow profiles (2017, methodology): “In many cases it is used for the former, to classify curves into groups, for example in Ferraty and Vieu (2003), Nerini and Ghattas (2007) and Guardiola et al. (2014).”
Random forests for functional covariates (2016, background): “The aim is to predict the fat content from the spectra or to classify meat samples into two groups defined by < 20% vs ≥ 20% fat content.(35) The data set contains NIR spectra of i = 1,.”
Ordinary kriging for function-valued spatial data (2011, methodology): “…exploratory and descriptive data analysis (Ramsay and Silverman 2005), linear models (Cardot et al. 1999; Ramsay and Silverman 2005), non-parametric methods (Ferraty and Vieu 2006) or multivariate techniques (Goulard and Voltz 1993; Voltz and Goulard 1994; Silverman 1995; Ferraty and Vieu 2003).”
Curve alignment by moments (2007, background): “…functional predictors [Ferraty and Vieu (2002) and James and Silverman (2005)], functional linear discriminant analysis [James and Hastie (2001) and Ferraty and Vieu (2003)] and functional clustering [James and Sugar (2003) and Bar-Joseph et al. (2003)] all assume that the starting curves are…”
Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels (2008, background): “Ferraty and Vieu (2003) and Cuesta-Albertos and Fraiman (2007) considered the classification and discrimination of random curves, and Cuevas et al. (2004) developed the functional analysis of variance.”
An anova test for functional data (2004, background): “, 1999; Pezzulli and Silverman, 1993; Silverman, 1996), discriminant analysis (Ferraty and Vieu, 2003) and regression (Cardot et al.”
Achieving near perfect classification for functional data (2012, methodology): “As usual with chemometrics data, we worked with the derivative curves of the spectra, which we estimated with splines as in Ferraty and Vieu (2006). The”