Parametric or nonparametric: The FIC approach

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Parametric or Nonparametric? a Parametricness Index for Model Selection

In model selection literature two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike’s information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric s...

To “ Parametric or Nonparametric ? a Parametricness Index for Model Selection ”

BIC is used to select the order of polynomial regression between 1 and 30. The estimated σ from the selected model is used to calculate the PI. Representative scatterplots at n = 200 with σ1 = 3, σ2 = 7 can be found in Figure 1. Note that the function estimate based on the selected model by BIC is visually more different from that based on the smaller model with one fewer term for the parametri...

Parametric Versus Nonparametric Approach to Wiener Systems Identification

The problem of nonlinear dynamic systems modelling by means of block-oriented models has been strongly elaborated for the last four decades, due to vast variety of applications. The concept of block-oriented models assumes that the real plant, as a whole, can be treated as a system of interconnected blocks, static nonlinearities (N) and linear dynamics (L), where the interaction signals cannot ...

A Comparative Study Of Parametric And Nonparametric Regressions