Titre : On the excess of average squared error for data-driven bandwidths in nonparametric trend estimation 

Auteurs : Karim Benhenni, Didier A. Girard, Sana Louhichi,  

Revue : Advances in Pure and Applied Mathematics 

Numéro : Issue 3 (Special AUS-ICMS 2020) 

Volume : 12 

Date : 2021/07/28 

DOI : 10.21494/ISTE.OP.2021.0696 

ISSN : 1869-6090 

Résumé : We consider the problem of the optimal selection of the smoothing parameter $$${h}$$$ in kernel estimation of a trend in nonparametric regression models with strictly stationary errors. We suppose that the errors are stochastic volatility sequences. Three types of volatility sequences are studied: the log-normal volatility, the Gamma volatility and the log-linear volatility with Bernoulli innovations. We take the weighted average squared error (ASE) as the global measure of performance of the trend estimation using $$${h}$$$ and we study two classical criteria for selecting $$${h}$$$ from the data, namely the adjusted generalized cross validation and Mallows-type criteria. We establish the asymptotic distribution of the gap between the ASE evaluated at one of these selectors and the smallest possible ASE. A Monte-Carlo simulation for a log-normal stochastic volatility model illustrates that this asymptotic approximation can be accurate even for small sample sizes. 

Éditeur : ISTE OpenScience