Conditional risk estimate for functional data under strong mixing conditions

DOI

10.2991/jsta.2015.14.3.7How to use a DOI?

Keywords

Functional data; Kernel conditional hazard function; Kernel estimation; Probabilities of small balls; a-mixing.

Abstract

We consider the problem of nonparametric estimation of the conditional hazard function for functional mixing data. More precisely, given a strictly stationary random variables Zi = (Xi; Yi)i2N, we investigate a kernel estimate of the conditional hazard function of univariate response variable Yi given the functional variable Xi. The principal aim of this paper is to give the mean squared convergence rate and to prove the asymptotic normality of the proposed estimator.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Abbes Rabhi
AU  - Sara Soltani
AU  - Aboubacar Traore
PY  - 2015
DA  - 2015/09/01
TI  - Conditional risk estimate for functional data under strong mixing conditions
JO  - Journal of Statistical Theory and Applications
SP  - 301
EP  - 323
VL  - 14
IS  - 3
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2015.14.3.7
DO  - 10.2991/jsta.2015.14.3.7
ID  - Rabhi2015
ER  -