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- W146875177 abstract "Linear discriminant analysis with binary response is considered when the predictor is a functional random variable $$X={X_{t},tin [0,T]}$$ , $$T inmathbb{R}$$ . Motivated by a food industry problem, we develop a methodology to anticipate the prediction by determining the smallest $$T^{*}$$ , $$T^{*} leq T$$ , such that $$X^{*} = {X_{t}, tin [0,T^{*}]}$$ and X give similar predictions. The adaptive prediction concerns the observation of a new curve ω on $$[0, T^{*}(omega)]$$ instead of [0, T] and answers to the question “How long should we observe ω ( $$T^{*}(omega)=?$$ ) for having the same prediction as on [0,T] ?”. We answer to this question by defining a conservation measure with respect to the class the new curve is predicted." @default.
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- W146875177 date "2010-01-01" @default.
- W146875177 modified "2023-10-11" @default.
- W146875177 title "Anticipated and Adaptive Prediction in Functional Discriminant Analysis" @default.
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- W146875177 doi "https://doi.org/10.1007/978-3-7908-2604-3_17" @default.
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