Reference Interval Computation Using Robust vs Parametric and Nonparametric Analyses

The comparison of parametric and nonparametric bootstrap methods for reference interval computation in small sample size groups

According to the IFCC, to determine the population-based reference interval (RI) of a test, 120 reference individuals are required. However, for some age groups such as newborns and preterm babies, it is difficult to obtain enough reference individuals. In this study, we consider both parametric and nonparametric bootstrap methods for estimating RIs and the associated confidence intervals (CIs)...

Bayesian Nonparametric and Parametric Inference

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

Robust Simulation and Design Using Parametric Interval Methods

A method is presented for guaranteeing robust steady-state operation of chemical processes using a model-based approach, taking into account uncertainty in the model parameters and disturbances in the process inputs. Intractable bilevel optimization formulations have been proposed for this problem in the past. A new approach is presented in which the equality constraints (process model equation...

A Comparative Study Of Parametric And Nonparametric Regressions

Model Selection Error Rates in Nonparametric vs. Parametric Model Comparisons

Since the introduction of Akaike’s information criteria (AIC) in 1973, many information criteria have been developed and widely used in model selection. Many papers concerning the justification of criteria followed, particularly with respect to model selection error rates (the probability of selecting a wrong model). A model selection criterion is called consistent if the model selection error ...