Neumann, Michael H.Michael H.NeumannLeucht, AnneAnneLeucht2026-05-212026-05-212026https://fis.uni-bamberg.de/handle/uniba/115192For time series data observed at non-random and possibly nonequidistant time points, we estimate the trend function nonparametrically. Under the assumption of a bounded total variation of the function and low-order moment conditions on the errors we propose a nonlinear wavelet estimator which uses a Haar-type basis adapted to a possibly non-dyadic sample size. An appropriate thresholding scheme for sparse signals with an additive polynomial-tailed noise is frst derived in an abstract framework and then applied to the problem of trend estimation.engtime seriestrend estimationwavelet thresholdingpreprinturn:nbn:de:bvb:473-irb-115192x