CHAVENT, Marie; GIRARD, Stéphane; KUENTZ, Vanessa; LIQUET, Benoit; NGUYEN, Thi Mong Ngoc; SARACCO, Jérôme

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CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]

GIRARD, Stéphane
Modelling and Inference of Complex and Structured Stochastic Systems [MISTIS]

KUENTZ, Vanessa
Aménités et dynamiques des espaces ruraux [UR ADBX]

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Communication dans un congrès

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XIVth International Symposium of Applied Stochastic Models and Data Analysis (ASMDA 2011), XIVth International Symposium of Applied Stochastic Models and Data Analysis (ASMDA 2011), ASMDA 2011 - XIVth International Symposium of Applied Stochastic Models and Data Analysis, 2011-06-07, Rome. 2011-06p. 257-264

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In this communication, we consider block-wise evolving data streams. When a semiparametric regression model involving a common dimension reduction direction B is assumed for each block, we propose an adaptive SIR (for sliced inverse regression) estimator of B. This estimator is faster than usual SIR applied to the union of all the blocks, both from computational complexity and running time points of view. We show the consistency of our estimator at the root-n rate. In a simulation, we illustrate the good numerical behaviour of the estimator. We also provide a graphical tool in order to detect if there exists a drift of the dimension reduction direction or some aberrant blocks of data. We illustrate our approach with various scenarios. Finally, possible extensions of this method are given.< Réduire

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regression

numerical model

numerical simulation

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An adaptive SIR method for block-wise evolving data streams

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