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Estimation in functional linear quantile regression
This paper studies estimation in functional linear quantile regression in
which the dependent variable is scalar while the covariate is a function, and
the conditional quantile for each fixed quantile index is modeled as a linear
functional of the covariate. Here we suppose that covariates are discretely
observed and sampling points may differ across subjects, where the number of
measurements per subject increases as the sample size. Also, we allow the
quantile index to vary over a given subset of the open unit interval, so the
slope function is a function of two variables: (typically) time and quantile
index. Likewise, the conditional quantile function is a function of the
quantile index and the covariate. We consider an estimator for the slope
function based on the principal component basis. An estimator for the
conditional quantile function is obtained by a plug-in method. Since the
so-constructed plug-in estimator not necessarily satisfies the monotonicity
constraint with respect to the quantile index, we also consider a class of
monotonized estimators for the conditional quantile function. We establish
rates of convergence for these estimators under suitable norms, showing that
these rates are optimal in a minimax sense under some smoothness assumptions on
the covariance kernel of the covariate and the slope function. Empirical choice
of the cutoff level is studied by using simulations.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1066 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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