TY  - JOUR
ID  - Slaoui2019
LB  - Slaoui2019
AU  - Slaoui, Yousri
TI  - Large and moderate deviation principles for nonparametric recursive kernel distribution estimators defined by stochastic approximation method
ST  - Large and moderate deviation principles for nonparametric recursive kernel distribution estimators defined by stochastic approximation method
JO  - Opuscula Math.
JA  - Opuscula Math.
JF  - Opuscula Mathematica
PY  - 2019
VL  - 39
IS  - 5
SP  - 733
EP  - 746
DO  - https://doi.org/10.7494/OpMath.2019.39.5.733
UR  - https://doi.org/10.7494/OpMath.2019.39.5.733
AB  - In this paper we prove large and moderate deviations principles for the recursive kernel estimators of a distribution function defined by the stochastic approximation algorithm. We show that the estimator constructed using the stepsize which minimize the Mean Integrated Squared Error (MISE) of the class of the recursive estimators defined by Mokkadem et al. gives the same pointwise large deviations principle (LDP) and moderate deviations principle (MDP) as the Nadaraya kernel distribution estimator.
KW  - distribution estimation
KW  - stochastic approximation algorithm
KW  - large and moderate deviations principles
SN  - 1232-9274
ER  -