Bootstrap approach in nonlinear regression model

Abstract

Nonlinear regression models are of considerable practical importance in a variety of applications. Unfortunately, conventional methods of point and interval estimation depend on a normality assumption for error and only asymptotically correct linearizing approximations to the regression surface. In this paper we will compare bootstrap method to the known methods, conventional method based on normality assumption for random error, jackknife, in nonlinear regression models.