Regression on functional data: methodological approach with application to near-infrared spectrometry

Ferraty, Frédéric

Journal de la société française de statistique, Tome 155 (2014) no. 2, pp. 100-120.

On s’intéresse à la situation où on observe une variable réponse réelle ainsi qu’une variable fonctionnelle comme prédicteur. Pour fixer les idées, dans notre problème issu de l’industrie pétrolière, la variable réponse correspond à l’indice d’octane d’un échantillon d’essence alors que la variable explicative représente son spectre dans le proche infrarouge. La communauté statisticienne a développé de nombreux modèles pour traiter de tels jeux de données et nous nous concentrerons particulièrement sur quatre d’entre eux : deux standards à l’instar du modèle de régression linéaire fonctionnelle et de la régression nonparamétrique fonctionnelle, et deux récemment développés : la régression fonctionnelle à directions révélatrices et un modèle parcimonieux basé sur une méthode de sélection nonparamétrique de variables. Chacune de ces méthodes sont mises en oeuvre avec deux jeux de données contenant des spectres dans le proche infrarouge. Une étude comparative de ces modèles est réalisée afin d’identifier les éventuels avantages et inconvénients de chacun d’eux. Pour finir, nous proposons dans une démarche méthodologique de rendre plus performants les deux modèles de régression les plus récents en tenant compte des informations les plus pertinentes obtenues par chacun des modèles étudiés. Nous montrons sur les données spectrométriques comment une telle démarche peut conduire à d’importantes améliorations.

We consider the situation when one observes a scalar response and a functional variable as predictor. For instance, in our petroleum industry problem, the response is the octane number of a gasoline sample and the functional predictor is a curve representing its near-infrared spectrum. The statistician community developed numerous models for handling such datasets and we focus here on four regression models: two standards as the functional linear model and the functional nonparametric regression, and two recently developed: the functional projection pursuit regression and a parsimonious model involving a nonparametric variable selection method. Each of these models are implemented with two datasets containing near-infrared spectrometric curves. A comparative study of these models is provided in order to emphasize their possible advantages and drawbacks. At last, a simple but useful methodological approach is then proposed in order to boost the two most recent regression models by combining the most relevant informations obtained by each of the studied models. We show on the spectrometric data how such an approach may lead to important improvements.

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