Title of article :
Modern nonlinear regression methods
Author/Authors :
Frank ، نويسنده , , Ildiko E.، نويسنده ,
Issue Information :
دوفصلنامه با شماره پیاپی سال 1995
Abstract :
Several nonparametric nonlinear regression models are discussed and compared. Instead of forcing a predefined analytical form on the data, these methods approximate the underlying nonlinear function using smoothers or splines on the training data set. The performances of these methods are compared in a Monte Carlo simulation study and illustrated on a data set from food chemistry.
Journal title :
Chemometrics and Intelligent Laboratory Systems
Journal title :
Chemometrics and Intelligent Laboratory Systems