Recursive non-parametric kernel classification rule estimation for independent functional data

Yousri Slaoui ()
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Yousri Slaoui: Université de Poitiers

Computational Statistics, 2021, vol. 36, issue 1, No 4, 79-112

Abstract: Abstract In this paper we propose an automatic selection of the bandwidth of the recursive non-parametric estimation of the kernel classification rule function defined by the stochastic approximation algorithm, when the explanatory data are curves and the response is categorical. We established a central limit theorem for our proposed recursive estimators, the proposed recursive estimators will be very competitive to the non-recursive one in terms of estimation error but much better in terms of computational costs. The proposed estimators are used first on simulated waveform curves and then on real phoneme data.

Keywords: Stochastic approximation algorithm; Asymptotic normality; Functional data; Regression estimation; Supervised classification; Smoothing; Curve fitting (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01024-9

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DOI: 10.1007/s00180-020-01024-9

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