Non-parametric Statistical Tools for the Social Sciences
Sometimes researchers do not a priori have a specific model or parametric assumption in mind when modeling social science data. Nonparametric approaches are useful in these circumstances for visualization as well as for suggesting underlying assumptions for subsequent parametric models. This talk reviews the basics of nonparametric data analysis from bivariate smoothing, kernels, and splines, to full regression-style generalized additive models.
