Flexible and Nonparametric Modeling
Jim E. Griffin, Fernando Andrés Quintana, Mark F. J. Steel · 2011
This article is divided into two parts. The first part considers flexible parametric models while the latter is nonparametric. It gives applications to regional growth data and semi parametric estimation of binomial proportions. It reviews methods for flexible mean regression, using either basis functions or Gaussian processes. This article also discusses Dirichlet processes and describes various posterior simulation algorithms for Bayesian nonparametric models. Usefulness is shown in empirical illustrations. Various applications as a function of income and as a cost function for electricity distribution are discussed. This article lists some freely available software that can accommodate many of the methods discussed. It provides a detailed discussion of both theory and computation for flexible treatment of distributions or functional forms or both.