Add to ORKGInternational audienceIn some applications, especially spectrometric ones, curve classifiers achieve better performances if they work on the $m$-order derivatives of their inputs. This paper proposes a smoothing spline based approach that give a strong theoretical background to this common practice
We derive sufficient conditions for sampling with derivatives in shift-invariant spaces generated by...
This thesis presents three novel statistical methods for the robust analysis of functional data and ...
This thesis consists of three chapters. The first chapter focuses on adaptive smoothing splines for ...
International audienceIn some applications, especially spectrometric ones, curve classifiers achieve...
Abstract. In some applications, especially spectrometric ones, curve classifiers achieve better perf...
International audienceIn some real world applications, such as spectrometry, functional models achie...
National audienceIn some real world applications, functional models achieve better predictive perfor...
peer-reviewedTraditional algorithms for modelling functional data use derivative-based optimisation ...
The sample observations of a functional variable are functions that come from the observation of a ...
We introduce a new model of linear regression for random functional inputs taking into account the f...
Observations that are realizations of some continuous process are frequently found in science, engin...
Derivative estimation is important in a wide range of disciplines. It is often the case that when an...
The estimation of curve derivatives is of interest in many disciplines. It allows the extraction of ...
We present a fully automated framework to estimate derivatives nonparametrically without estimating ...
In many situations it is of primary interest to estimate the rate of change of the relationship betw...
We derive sufficient conditions for sampling with derivatives in shift-invariant spaces generated by...
This thesis presents three novel statistical methods for the robust analysis of functional data and ...
This thesis consists of three chapters. The first chapter focuses on adaptive smoothing splines for ...
International audienceIn some applications, especially spectrometric ones, curve classifiers achieve...
Abstract. In some applications, especially spectrometric ones, curve classifiers achieve better perf...
International audienceIn some real world applications, such as spectrometry, functional models achie...
National audienceIn some real world applications, functional models achieve better predictive perfor...
peer-reviewedTraditional algorithms for modelling functional data use derivative-based optimisation ...
The sample observations of a functional variable are functions that come from the observation of a ...
We introduce a new model of linear regression for random functional inputs taking into account the f...
Observations that are realizations of some continuous process are frequently found in science, engin...
Derivative estimation is important in a wide range of disciplines. It is often the case that when an...
The estimation of curve derivatives is of interest in many disciplines. It allows the extraction of ...
We present a fully automated framework to estimate derivatives nonparametrically without estimating ...
In many situations it is of primary interest to estimate the rate of change of the relationship betw...
We derive sufficient conditions for sampling with derivatives in shift-invariant spaces generated by...
This thesis presents three novel statistical methods for the robust analysis of functional data and ...
This thesis consists of three chapters. The first chapter focuses on adaptive smoothing splines for ...