ABSTRACT
In this chapter in Section 4.1 the definition of nonlinear regression model is presented. In Section 4.2 estimation of regression parameters is discussed by first considering least squares and then the maximum likelihood method. Then in Section 4.3 the approximate distribution of likelihood ratio statistic is presented and applied in Section 4.4 and Section 4.5 to derive profile likelihood-based regions to whole or part of the vector of the regression parameters and profile likelihood-based confidence interval to any smooth realvalued function of the regression parameters, respectively. In Section 4.6 the likelihood ratio test of a nonlinear hypothesis on the regression parameters is considered. Finally, Section 4.7 contains a discussion of model checking.
