Regression model for surrogate data in high dimensional statistics

Firas Ibrahim, Ali Hajj Hassan, Jacques Demongeot and Mustapha Rachdi

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 13, 3206-3227

Abstract: This paper deals with the problem of estimating the regression of a surrogated scalar response variable given a functional random one. We construct an estimator of the regression operator by using, in addition to the available (true) response data, a surrogate data. We then establish some asymptotic properties of the constructed estimator in terms of the almost-complete and the quadratic mean convergences. Notice that the obtained results generalize a part of the results obtained in the finite dimensional framework. Finally, an illustration on the applicability of our results on both simulated data and a real dataset was realized. We have thus shown the superiority of our estimator on classical estimators when we are lacking complete data.

Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1080/03610926.2019.1586940

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