Kink estimation with correlated noise

Justin Wishart*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this article we study the 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). The method is based on the zero-crossing technique and makes use of high-order kernels. The effect of LRD is seen to be detrimental to the rate of convergence. Using a fractional integration operator we draw a parallel with certain inverse problems which suggests optimality of our approach. The kink location and estimation technique is demonstrated on some simulated data and the detrimental effect of LRD is shown. We also apply our kink analysis on Australian temperature data.

Original languageEnglish
Pages (from-to)131-143
Number of pages13
JournalJournal of the Korean Statistical Society
Issue number2
Publication statusPublished - Jun 2009
Externally publishedYes


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