Smoothed L-estimation of regression function

P. Cizek, J. Tamine, W.K. Härdle

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

Abstract

The Nadaraya–Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data. This sensitivity can be reduced, for example, by using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. The asymptotic distribution of the proposed estimator is derived under mild β-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical finite-sample improvements. Finally, the proposed method is applied to the modelling of implied volatility.
Original languageEnglish
Pages (from-to)5154-5162
JournalComputational Statistics & Data Analysis
Volume52
Publication statusPublished - 2008

Fingerprint Dive into the research topics of 'Smoothed L-estimation of regression function'. Together they form a unique fingerprint.

  • Cite this