B0885
Title: Difference-based estimators of (autoco)variance in nonparametric models with a discontinuous signal
Authors: Inder Tecuapetla-Gomez - CONACyT-CONABIO (Mexico) [presenting]
Abstract: A class of difference-based estimators for the (autoco)variance in nonparametric regression is discussed for the case when the signal is discontinuous (change-point regression), possibly highly fluctuating, and the errors form a stationary m-dependent process. These estimators circumvent the explicit pre-estimation of the unknown regression function, a task which is particularly challenging for these signals. We derive their finite sample mean squared errors when the signal function is piecewise constant (segment regression) and the errors are Gaussian; based on this we derive biased-optimized estimates which do not depend on the particular (unknown) autocovariance structure. Notably, for positively correlated errors, that part of the variance of our estimators which depends on the signal is minimal as well. Asymptotic properties of these estimators in this and more general change-point models will be discussed as well. Simulation studies and an application to biophysical measurements will be shown.
Journal