Regression rank scores in nonlinear models

Consider the nonlinear regression model $Y_i=g({\mathbf{x}}_i,\text{boldmath}$\theta$)+e_i,i=1,\mathrm{.}\mathrm{.}\mathrm{.},n$(1) with ${\mathbf{x}}_i\in {\mathbb{R}}^k,$ $\text{boldmath}\theta =({\theta }_0,{\theta }_1,\mathrm{.}\mathrm{.}\mathrm{.},{\theta }_p{)}^{\mathrm{\prime }}\in \text{boldmath}$\Theta(compact in $\mathbb{R}^{p+1}$), where $g({\bf x},\boldmath $\theta$)=\theta_0+\tilde{g}({\bf x},\theta_1,...,\theta_p)$ is continuous, twice differentiable in $\boldmath $\theta and monotone in components of $\text{boldmath}$\theta$$. Following Gutenbrunner and Jure\v{c}kov'{a} (1992) and Jure\v{c}kov'{a} and Proch'{a}zka (1994), we introduce regression rank scores for model (1), and prove their asymptotic properties under some regularity conditions. As an application, we propose some tests in nonlinear regression models with nuisance parameters.