On bandwidth selection problems in nonparametric trend estimation under martingale difference errors

Karim Benhenni (1) , Didier A. Girard (1) , Sana Louhichi (1)

1 IPS - Inférence Processus Stochastiques

In this paper, we are interested in the problem of smoothing parameter selection in nonparametric curve estimation under dependent errors. We focus on kernel estimation and the case when the errors form a general stationary sequence of martingale difference random variables where neither linearity assumption nor "all moments are finite" are required. We compare the behaviors of the smoothing bandwidths obtained by minimizing three criteria: the average squared error, the mean average squared error and a Mallows-type criterion adapted to the dependent case. We prove that these three minimizers are first-order equivalent in probability. We give also a normal asymptotic behavior of the gap between the minimizer of the average square error and that of the Mallows-type criterion. Finally, we apply our theoretical results to a specific case of martingale difference sequence, namely the Auto-Regressive Conditional Heteroscedastic (ARCH(1)) process. A Monte-carlo simulation study, for this regression model with ARCH(1) process, is conducted.

Mots clés: Mean average squared error; Cross validation; Martingale difference sequences; Average squared error; Nonparametric trend estimation; Mallows criterion; Kernel nonparametric models; Generalized cross validation; Kernel smoothing; Bandwidth selection

Domaines: Statistiques [math.ST]

Karim Benhenni, Didier A. Girard, Sana Louhichi. On bandwidth selection problems in nonparametric trend estimation under martingale difference errors. 2020. ⟨hal-02514827v1⟩