Asbtract:  In this seminar we study the non-parametric estimation of the location of jump points in the first derivative (referred to as kinks) of a regression function f in the presence of noise that exhibits long-range dependence (LRD). In particular, we consider the situation when observations are derived from a direct model with regular design points. The method is based on the zero-crossing technique and makes use of high-order kernels and is optimal in the minimax sense. The kink location and estimation technique is demonstrated on some simulated data and the detrimental effect of LRD on the rate of convergence is shown. We also apply our kink analysis on Australian temperature dataset as an example.