A partial overview of the theory of statistics with functional data

By Antonio Cuevas. 2014

386 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f9af9bf6fe9f28c1851d8d47bcc80dbb3da7807a

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Variable selection in functional data classification: a maxima-hunting proposal (2013, methodology): “on (Hsing and Ren (2009); Jiang, Yu and Wang (2013)) and additive models (Zhang, Park and Wang (2013)). Also, the methods based on random projections could oer an interesting alternative. See, e.g., Cuevas (2014) for a short overview of dimension-reduction techniques together with additional referen”
Modelling function‐valued stochastic processes, with applications to fertility dynamics (2017, methodology): “In related applications such as mortality analysis, the decomposition into age and year has been studied by Eilers and Marx (2003); Currie et al. (2004, 2006); Eilers et al. (2006), using P-splines.”
Extensions of some classical methods in change point analysis (2014, methodology): “Introduction and thorough reviews of functional data methods are given in Cuevas (2014), Ferraty and Romain (2011) and Horváth and Kokoszka (2012).”
Interval-wise testing for functional data (2017, methodology): “Functional data analysis (FDA), the analysis of data sets characterised by the observations of one or more functions for each statistical unit, has recently achieved a considerable attention in modern statistical scientific investigation (see, e.g. Ramsay and Silverman 2002, 2005; Ferraty and Vieu 2”
On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification (2015, background): “In the booming ﬁeld of statistics with functional data [see Cuevas (2014) for a recent survey], the computational and numerical aspects, as well as the real data applications, have had (un-derstandably) a major role so far.”
Peter Hall, functional data analysis and random objects (2016, background): “Peter also left a substantial legacy of works on functional classification, which recently has become a very active line of research (Cuevas, 2014).”
Nonparametric modelling for functional data: selected survey and tracks for future (2018, background): “[11], as well as those selected bibliographical discussions byGeenens [12], Cuevas [13], Jacques andPreda [14], Shang [15], Horváth and Rice [16], Müller [17], Goia and Vieu [18], and Nagy [19], highlight the wide scope of recent advances in FDA.”