Smoothing dichotomy in randomized fixed-design regression with strongly dependent errors based on a moving average

• Autorzy: Artur Bryk
• Języki publikacji: EN

Abstrakty

EN

We consider a fixed-design regression model with errors which form a Borel measurable function of a long-range dependent moving average process. We introduce an artificial randomization of grid points at which observations are taken in order to diminish the impact of strong dependence. We show that the Priestley-Chao kernel estimator of the regression fuction exhibits a dichotomous asymptotic behaviour depending on the amount of smoothing employed. Moreover, the resulting estimator is shown to exhibit weak consistency (i.e. in probability). Simulation results indicate significant improvement when randomization is employed.

Kategorie tematyczne

• 62G20: Asymptotic properties
• 62G08: Nonparametric regression
• 62M10: Time series, auto-correlation, regression, etc.

• Wydawca: Institute of Mathematics Polish Academy of Sciences
• Czasopismo: Applicationes Mathematicae
• Rocznik: 2014
• Tom: 41
• Numer: 1
• Strony: 67-80

Daty

wydano

2014

Twórcy

autor

Artur Bryk

• Department of Mathematics and Mathematical Economics, Warsaw School of Economics, Al. Niepodległości 162, 02-554 Warszawa, Poland

Identyfikatory

DOI

10.4064/am41-1-6