Nadine Hilgert

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Estimating the innovation probability density is an important issue in any regression analysis. This paper focuses on functional autore-gressive models. A residual-based kernel estimator is proposed for the innovation density. Asymptotic properties of this estimator depend on the average prediction error of the functional autoregressive function. Sufficient(More)
We consider a class of discrete-time stochastic control systems, with Borel state and action spaces, and possibly unbounded costs. The processes evolve according to the equation x t+1 = F (x t , a t , ξ t), t = 0, 1,. .. , where the ξ t are i.i.d. random vectors whose common distribution is unknown. Assuming observability of {ξ t }, we use the empirical(More)
This work proposes a framework using temporal data and domain knowledge in order to analyze complex agronomical features. The expertise is rst formalized in an ontology, under the form of concepts and relationships between them, and then used in conjunction with raw data and mathematical models to design a software sensor. Next the software sensor outputs(More)
This work proposes a framework using temporal data and domain knowledge in order to analyze complex agronomical features. The expertise is first formalized in an ontology, under the form of concepts and relationships between them, and then used in conjunction with raw data and mathematical models to design a software sensor. Next the software sensor outputs(More)
This paper is devoted to a short presentation of the use we did of nonparametric estimation theory for the estimation, filtering and control of uncertain dynamic systems. The fundamental advantage of this approach is its low dependence from any a priori modeling assumptions about uncertain dynamic components. It appears to be of great interest for the(More)
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