Nonlinear wavelet estimator of the regression function under left-truncated dependent data
Jacobo de Uña‐Álvarez, Han‐Ying Liang, Alberto Rodríguez‐Casal · Journal of nonparametric statistics · 2010
In this paper, we define a new nonlinear wavelet-based estimator of the regression function under random left-truncation. We provide an asymptotic expression for the mean integrated squared error (MISE) of the estimator. It is assumed that the observations form a stationary α-mixing sequence. The nonlinear wavelet-based estimator of the covariate's density is considered as well. Unlike for kernel estimators, the MISE expression of the wavelet-based estimators is not affected by the presence of discontinuities in the curves. The finite sample behaviour of the proposed estimators is explored through simulations.