We review Bayesian and classical approaches to nonparametric density and regression estimation and illustrate how these techniques can be used in economic applications. On the Bayesian side, density estimation is illustrated via finite Gaussian mixtures and a Dirichlet Process Mixture Model, while nonparametric regression is handled using priors that impose smoothness. From the frequentist perspective, kernel-based nonparametric regression techniques are presented for both density and regression problems. Both approaches are illustrated using a wage dataset from the Current Population Survey. WIREs Comput Stat 2017, 9:e1406. doi: 10.1002/wics.1406. For further resources related to this article, please visit the WIREs website.
|Original language||English (US)|
|Journal||Wiley Interdisciplinary Reviews: Computational Statistics|
|State||Published - Nov 1 2017|
ASJC Scopus subject areas
- Statistics and Probability