We determine optimal designs for some regression models which are frequently used for describing 3D shapes. These … information matrix in the spherical harmonic regression model as the uniform distribution and are therefore directly implementable …

common linear and nonparametric regression model, which are based on empirical processes of residuals. It is well known that … linear and nonparametric regression models. …

In this paper we present a detailed numerical comparison of three monotone nonparametric kernel regression estimates … Pilz (2003) and combines density and regression estimation techniques to obtain a monotone curve estimate of the inverse of … the isotone regression function. The three concepts are briefly reviewed and their finite sample properties are studied by …

A monotone estimate of the conditional variance function in a heteroscedastic, nonpara- metric regression model is …

In this note we consider several goodness-of-fit tests for model specification in non- parametric regression models … asymptotically optimal bandwidth for the estimation of the regression function. We prove weak convergence of these processes to …

We consider maximin and Bayesian D-optimal designs for nonlinear regression models. The maximin criterion requires the … paper we present a rigorous proof of this phenomenon and show that in many nonlinear regression models the number of support …

For the Weibull- and Richards-regression model robust designs are determined by maximizing a minimum of D- or D1 … postulated form of the regression model against a simplified sub-model. …

In the common Fourier regression model we determine the optimal designs for estimating the coefficients corresponding …

In this article a new monotone nonparametric estimate for a regression function of two or more variables is proposed …. The method starts with an unconstrained nonparametric regression estimate and uses successively one …-dimensional isotonization procedures. In the case of a strictly monotone regression function, it is shown that the new estimate is first order …