Editorial on the special issue on Functional Data Analysis and Related Topics

Aneiros, Germán; Cao, Ricardo; Vieu, Philippe

doi:10.1007/s00180-019-00892-0

Cited by 21 publications

(11 citation statements)

References 14 publications

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“…FDA was 20 ago a very confidential area of researches and its popularization started really with Ramsay and Silverman's books (see Ramsay & Silverman, 1996, 2002, 2005). Twenty years later, many among standard multivariate statistical procedures have been adapted to functional data and there is now a wide literature on FDA as attested by the books (Aneiros, Bongiorno, Cao, & Vieu, 2017; Bongiorno, Goia, Salinelli, & Vieu, 2014; Bosq, 2000; Bosq & Blanke, 2007; Ferraty & Vieu, 2006; Horváth & Kokoszka, 2012; Hsing & Eubank, 2015; Shi & Choi, 2011; Zhang, 2013a), and by various bibliographical discussions such as Aneiros, Cao, and Vieu (2019), Aneiros, Cao, Fraiman, Genest, and Vieu (2019), Cuevas (2014), Geenens (2011), Goia and Vieu (2016), Horváth and Rice (2015), Jacques and Preda (2014), Ling and Vieu (2018), Müller and Hall (2016), Nagy (2017), Shang (2014a), and Vieu (2018).…”

Section: Introductionmentioning

confidence: 99%

“…The recent literature provides a wide scope of applied scientific fields having to face with continuous data and for which such kind of functional regression models is useful. This contribution is deliberately based on a discussion on methodological advances and, concerning many possible general examples, we refer to Ullah and Finch (2013), Ferraty and Vieu (2002), or Aneiros, Cao, and Vieu (2019) for details. Even if in any practical problem the functional variables are only observed on a discrete grid, this grid becomes in reality more and more fine and this is the aim of the model ) to deal with these data in a continuous way as they are in reality.…”

Section: Introductionmentioning

confidence: 99%

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On semiparametric regression in functional data analysis

Ling

Vieu

2020

WIREs Computational Stats

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The aim of this paper is to provide a selected advanced review on semiparametric regression which is an emergent promising field of researches in functional data analysis. As a deliberate strategy, we decided to focus our discussion on the single functional index regression (SFIR) model in order to fix the ideas about the stakes linked with infinite dimensional problems and about the methodological challenges that one has to solve when building statistical procedure: one of the most challenging issue being the question of dimensionality effects reduction. This will be the first (and the main) part of this discussion and a complete survey of the literature on SFIR model will be presented. In a second attempt, other semiparametric models (and more generally, other dimension reduction models) will be shortly discussed with the double goal of presenting the state of art and of defining challenging tracks for the future. At the end, we will discuss how additive modeling is an appealing idea for more complicated models involving multifunctional predictors and some tracks for the future will be pointed in this setting. This article is categorized under: Statistical Models > Semiparametric Models Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On semiparametric regression in functional data analysis

Ling

Vieu

2020

WIREs Computational Stats

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“…Finally, a large spectrum of very recent results in the field of FDA is covered in two JMVA Special Issues on Functional Data Analysis and Related Topics (Goia and Vieu 2016;Aneiros et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Testing serial independence with functional data

Hlávka

Hušková

Meintanis

2020

TEST

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We consider tests of serial independence for a sequence of functional observations. The new methods are formulated as L2-type criteria based on empirical characteristic functions and are convenient from the computational point of view. We derive asymptotic normality of the proposed test statistics for both discretely and continuously observed functions. In a Monte Carlo study, we show that the new test is sensitive with respect to functional GARCH alternatives, investigate the choice of necessary tuning parameters, and demonstrate that critical values obtained by resampling lead to a test with good performance in any setup, whereas the asymptotic critical values may be recommended only for a sufficiently fine discretization grid. Finite-sample comparison with a distance (auto)covariance test criterion is also included, and the article concludes with application on a real data set.

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“…These new models are based on the notion of functional Statistics that allows to model the data as curves instead of some numerical numbers which permit to explore the total information of the data. For an overview on recent developments in this topic, we cite, some special issues dedicated to this topic by various statistical journals such as Gonzalez- Manteiga and Vieu (2007), Valderrama (2007), Goia and Vieu (2016) and Aneiros et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Modern Statistical Analysis of Forage Quality Assessment With Nir Spectroscopy

Almanjahie¹

2019

Appl. Ecol. Env. Res.

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Recently, the statisticians have developed a new approach called functional Statistics to treat the data as curves or images. In parallel, the Near-Infrared Reflectance (NIR) spectroscopy approach has been used in modern Chemistry being a fast, inexpensive and accurate procedure to characterize chemical properties for an object. In this paper, we study the forage quality by analysing the spectroscopy procedure with some modern statistical models. Our contribution leads to the prediction of chemical components of Chinese ryegrass forage by analysing its spectral data using some functional models. Precisely, the functional linear quantile regression (FLQR), the functional nonparametric quantile regression (FNQR), the functional local linear quantile regression (FLLQR) and the functional local linear model regression (FLLMR) are implemented to predict the quantities of acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP) contents. The choice of these functional models is motivated by the fact that they can construct a predictive region with a given confidence level. We show that the considered models improve the prediction results significantly as compared to conventional models such as the classical partial least squares regression (PLSR) and the principal component regression (PCR). Moreover, we also show that the proposed models are more robust than their competitive models like PLSR and PCR in the sense that their efficiency is not much affected by nonhomogeneity of the data.

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Editorial on the special issue on Functional Data Analysis and Related Topics

Cited by 21 publications

References 14 publications

On semiparametric regression in functional data analysis

On semiparametric regression in functional data analysis

Testing serial independence with functional data

Modern Statistical Analysis of Forage Quality Assessment With Nir Spectroscopy

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