Abstract
This paper compares the small-sample properties of two non-parametric regression methods, running interval smoother and constrained b-spline smoothing. The running interval smoother method deals with estimation of a conditional quantile (or a measure of location) using different estimators and here our focus is on Harrell-Davis and newly proposed NO quantile estimators. The constrained b-spline smoothing method uses the quantile regression estimator while obtaining conditional quantile estimates. Constrained b-spline smoothing and running interval smoother methods are compared with a simulation study by using theoretical distributions. Furthermore, the methods are examined graphically to understand how they can model the relationship between variables. Constrained b-spline smoothing and running interval smoother with NO estimator outperformed running interval smoother with Harrell-Davis estimator in terms of mean squared error.
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