An anova test for functional data

By Antonio Cuevas, M. Febrero–Bande, Ricardo Fraiman. Computational Statistics & Data Analysis, 2004

497 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ff506f6bd302e9bb0e9fc7350957807e87d72bca

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Citations in the corpus

Two Samples Tests for Functional Data (2010, background): “( 2004 ) proposed an ANOVA test for functional data.”
Functional Data Analysis (2016, methodology): “Fan & Lin (1998) proposed a two-sample test and ANOVA test for the mean functions, with further work by Cuevas, Febrero & Fraiman (2004) and Zhang (2013).”
Detecting changes in the mean of functional observations (2009, background): “The results of Dauxois et al. (1982) and Bosq (2000) then imply that for each k ≤ d:”
Second-Order Comparison of Gaussian Random Functions and the Geometry of DNA Minicircles (2010, methodology): “…for functional data is mostly concentrated on tests pertaining to the mean function (Fan and Lin 1998), as encountered, for instance, in functional linear models (Cardot et al. 2003; Cuevas, Febrero, and Fraiman 2004; Shen and Faraway 2004) or functional change detection (Berkes et al. 2009).”
Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels (2008, methodology): “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.”
Interval-wise testing for functional data (2017, background): “The former approach commonly relies on distributional assumptions on functional data and/or on asymptotic results (Cuevas et al. 2004; Abramovich and Angelini 2006; Antoniadis and Sapatinas 2007; Horv´ath and Kokoszka 2012; Staicu et al. 2014; Zhang and Liang 2014).”
Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis (2014, methodology): “…address this problem: Fan & Lin (1998) developed an adjusted Neyman testing procedure for independent stationary linear Gaussian processes; Cuevas et al. (2004) proposed an F -test for independent processes; Staicu et al. (2012) considered an L2-norm-based global testing procedure for…”
Multivariate analysis of variance for functional data (2017, methodology): “This paper is concernedwith the generalization of theMANOVAproblem to the functional setting.”
One‐Way anova for Functional Data via Globalizing the Pointwise F‐test (2014, methodology): “In this case, we can adopt the PB method of Cuevas et al. (2004) to obtain an approximate critical value of Tn for any given significance level ˛.”
Two-sample tests in functional data analysis starting from discrete data (2007, background): “Recent contributions to hypothesis testing in this field include those of Fan and Lin (1998), Locantore et al. (1999), Spitzner et al. (2003), Cuevas et al. (2004) and Shen and Faraway (2004).”
A Two Sample Distribution-Free Test for Functional Data with Application to a Diffusion Tensor Imaging Study of Multiple Sclerosis (2016, methodology): “Extension to k independent samples of curves was discussed in Cuevas et al. (2004), Estévez-Pérez and Vilar (2008), and Laukaitis and Račkauskas (2005), who proposed ANOVA-like testing procedures for testing the equality of mean functions. For detecting differences in the covariance functions of ind”
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).”