A new method for robust nonparametric regression
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Consider the problem of estimating the mean function underlying a set of noisy data. Least squares is appropriate if the error distribution of the noise is Gaussian, and if there is good reason to believe that the underlying function has some particular form. But what if the previous two assumptions fail to hold? In this regression setting, a robust method is one that is resistant against outliers, while a nonparametric method is one that allows the data to dictate the shape of the curve (rather than choosing the best parameters for a fit from a particular family). Although it is easy to find estimators that are either robust or nonparametric, the literature reveals very few that are both. In this thesis, a new method is proposed that uses the fact that the
Description
Advisor
Degree
Type
Keywords
Citation
Wang, Ferdinand Tsihung. "A new method for robust nonparametric regression." (1990) Diss., Rice University. https://hdl.handle.net/1911/16403.