Outlier Resistant Estimation in Difference-Based Semiparametric Partially Linear Models

The difference based estimation in partially linear models is an approach designed to estimate parametric component by using the ordinary least squares estimator after removing the nonparametric component from the model by differencing. However, it is known that least squares estimates do not provide useful information for the majority of data when the error distribution is not normal, particularly when the errors are heavy-tailed and when outliers are present in the dataset. This paper aims to find an outlierresistant fit that represents the information in the majority of the data by robustly estimating the parametric and the nonparametric components. Note: The following files were submitted by the author for peer review, but cannot be converted to PDF. You must view these files (e.g. movies) online.