Non parametric learning approach to estimate conditional quantiles in the dependent functional data case

作者：Henchiri Yousri^*

来源：Communications in Statistics - Theory and Methods, 2017, 46(9): 4369-4387.

DOI：10.1080/03610926.2015.1082592

• Conditional quantile
• exponentially strongly mixing
• functional data
• oracle inequality
• support vector machines
• weak consistency

In this paper, we focus on conditional quantile estimation when the covariates take their values in a bounded subspace of the functional space L-2(T), of square integrable random functions defined on some compact set T. We use a non parametric learning approach based on support vector machines (SVMs) technique. The main goal is to establish a weak consistency of the SVMs estimator of conditional quantile under exponentially strongly mixing functional input sequences. Our main result (the estimator satisfies an oracle inequality) extends a previous result for independent and identically distributed sample. We apply this estimator in practice through a real data set study.

• 出版日期2017