Data-driven <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" display="inline" overflow="scroll"><mml:mi>k</mml:mi></mml:math>NN estimation in nonparametric functional data analysis

Kara, Lydia-Zaitri; Laksaci, Ali; Rachdi, Mustapha; Vieu, Philippe

doi:10.1016/j.jmva.2016.09.016

Cited by 72 publications

(24 citation statements)

References 31 publications

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“…We plan to extend this work by considering Bernstein polynomials rather than kernels and to propose an adaptation of the estimators developed in Jmaei et al [30] and Slaoui and Jmaei [48] in the case of functional data. We plan also to compare these estimators to the kernel nearest-neighbor approach developed in Kara et al [32], the semi-parametric functional projection pursuit regression [11], the single index model [25], the partial linear models [4,35] and the sparse modeling approach [5].…”

Section: Discussionmentioning

confidence: 99%

Wild bootstrap bandwidth selection of recursive nonparametric relative regression for independent functional data

Slaoui

2019

Journal of Multivariate Analysis

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Section: Discussionmentioning

confidence: 99%

Wild bootstrap bandwidth selection of recursive nonparametric relative regression for independent functional data

Slaoui

2019

Journal of Multivariate Analysis

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“…The flexibility and efficiency of this method have motivated several authors to introduce this approach into functional statistics. Among the pioneering works in this theme, we cite Burba et al (2009), Karaa et al (2017), Kudraszow and Vieu (2013), and Lian (2011)…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution

Benlakhdar¹,

Rziza²,

Thami³

2020

human

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Image classification refers to an important process in computer vision. The purpose of this paper is to propose a novel approach named GGD-GMM and based on statistical modeling in the wavelet domain to describe textured images and rely on a number of principles that give its internal coherence and originality. Firstly, we propose a robust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform. Secondly, we implement the aforementioned algorithm and fit the result using the finite mixture gamma distribution (GMM). The results, obtained for two benchmark datasets show that the proposed algorithm has a good relevance as it provides higher classification accuracy than some other well-known models (Kohavi, 1995). Moreover, it shows other advantages relied upon Noise-resistant and rotation invariant.

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“…For functional data, natural measures of proximity involve notions of shape as those given by the similarities of derivatives or FPC scores (Shang, 2016). This suggests the use of semi-metrics as distance measures for many kernel-based functional regression estimators, including functional local-constant/linear estimators (Ferraty and Vieu, 2002;Baíllo and Grané, 2009;Barrientos-Marin et al, 2010;Berlinet et al, 2011), functional k-nearest neighbours (Burba et al, 2009;Kudraszow and Vieu, 2013;Kara et al, 2017), functional recursive kernel estimator (Amiri et al, 2014), and Reproducing Kernel Hilbert Space (RKHS) methods (Preda, 2007;Avery et al, 2014). To avoid having to choose a semi-metric if several are available, Ferraty and Vieu (2009) and Febrero-Bande and González-Manteiga (2013) suggested to combine kernel estimators constructed with different semi-metrics.…”

Section: Introductionmentioning

confidence: 99%

Tree-based boosting with functional data

Salibián‐Barrera

2021

Preprint

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In this article we propose a boosting algorithm for regression with functional explanatory variables and scalar responses. The algorithm uses decision trees constructed with multiple projections as the "base-learners", which we call "functional multi-index trees". We establish identifiability conditions for these trees and introduce two algorithms to compute them: one finds optimal projections over the entire tree, while the other one searches for a single optimal projection at each split.We use numerical experiments to investigate the performance of our method and compare it with several linear and nonlinear regression estimators, including recently proposed nonparametric and semiparametric functional additive estimators. Simulation studies show that the proposed method is consistently among the top performers, whereas the performance of any competitor relative to others can vary substantially across different settings. In a real example, we apply our method to predict electricity demand using price curves and show that our estimator provides better predictions compared to its competitors, especially when one adjusts for seasonality.

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Data-driven kNN estimation in nonparametric functional data analysis

Cited by 72 publications

References 31 publications

Wild bootstrap bandwidth selection of recursive nonparametric relative regression for independent functional data

Wild bootstrap bandwidth selection of recursive nonparametric relative regression for independent functional data

A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution

Tree-based boosting with functional data

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