Submitted by acf199
on
Applications of Constrained Spline Density Estimation
Abstract:
Density estimation methods often involve kernels, but there are advantages to using splines. Especially if the shape of the density is known to be decreasing, or unimodal, or bimodal, or if the research question involves the shape of the density, splines allow the shape assumptions to be readily implemented. In addition, spline estimators enjoy a faster convergence rate compared to kernel density estimators. Applications include testing unimodal versus multimodal density, estimating a deconvolution density, robust regression, and testing for sampling bias.