Nonparametric quantile regression for spatio-temporal processes
Authors: Soudeep Deb, Claudia Neves, Subhrajyoty Roy
Summary: On this paper, we develop a brand new and efficient strategy to nonparametric quantile regression that accommodates ultrahigh-dimensional information arising from spatio-temporal processes. This strategy proves advantageous in staving off computational challenges that represent recognized hindrances to present nonparametric quantile regression strategies when the variety of predictors is far bigger than the out there pattern measurement. We examine situations beneath which estimation is possible and of excellent total high quality and acquire sharp approximations that we make use of to devising statistical inference methodology. These embody simultaneous confidence intervals and checks of hypotheses, whose asymptotics is borne by a non-trivial practical central restrict theorem tailor-made to martingale variations. Moreover, we offer finite-sample outcomes via varied simulations which, accompanied by an illustrative software to real-worldesque information (on electrical energy demand), supply ensures on the efficiency of the proposed methodology