The independent additive errors linear model consists of a structure for the mean and a separate structure for the error distribution. The error structure may be parametric or it may be semiparametric. Under alternative values of the mean structure, the best fitting additive errors model has an error distribution which can be represented as the convolution of the actual error distribution and t...

Abstract The parametric differential method calculates the form factors without using approximations by polygons. Because of this it contributes to the improvement of the realism of the images synthesised through the radiosity method. This paper presents the most important characteristics of the parametric differential method as well as the results concerning its accuracy. Comparison of the for...

We take an approach to specify and control implicitly dened n-dimensional free-form solid primitives using parametric functions of n variables. Outside of the domain, the parametric function value increases or decreases rapidly that is not suitable to design geometric objects. In this paper, we introduce a functional clipping operation which allows a free-form primitive to be treated as traditi...

We consider situations where the a priori guidance provided by theoretical considerations indicates only that the function linking the endogenous and exogenous variables is monotone and concave (or convex). We present methods to evaluate the adequacy of a parametric functional form to represent the relationship given the minimal maintained assumption of monotonicity and concavity (or convexity)...

In the common nonparametric regression model the problem of testing for the parametric form of the conditional variance is considered. A stochastic process based on the difference between the empirical processes obtained from the standardized nonparametric residuals under the null hypothesis (of a specific parametric form of the variance function) and the alternative is introduced and its weak ...

The purpose of this chapter is to present the design, analysis, and simulation of a wide class of algorithms that can be used for online parameter identification of continuous-time plants. The online identification procedure involves the following three steps. Step 1. Lump the unknown parameters in a vector θ∗ and express them in the form of the parametric model SPM, DPM, B-SPM, or B-DPM. Step ...

Accurate estimation of an underlying function and its derivatives is one of the central problems in statistics. Parametric forms are often proposed based on the expert opinion or prior knowledge of the underlying function. However, these strict parametric assumptions may result in biased estimates when they are not completely accurate. Meanwhile, nonparametric smoothing methods, which do not im...

We obtain a robust, non-parametric, estimate of the Hub-ble constant from galaxy linear diameters calibrated using HST Cepheid distances. Our method is independent of the parametric form of the diameter function and the spatial distribution of galaxies and is insensitive to Malmquist bias. We include information on the galaxy rotation velocities ; unlike Tully-Fisher, however, we retain a fully...

The subject of discussion is health care production functions. The parametric two-factor Cobb–Douglas and transcendental logarithmic (translog) production functions are frequently used. However, empirical and theoretical work has often questioned the validity of the parametric Cobb–Douglas and translog as a model for the production of health care. The aim of this study is to propose a new flexi...

Test procedures are constructed for testing the goodness-of-fit in parametric regression models. The test statistic is in the form of an L2 distance between the empirical characteristic function of the residuals in a parametric regression fit and the corresponding empirical characteristic function of the residuals in a non-parametric regression fit. The asymptotic null distribution as well as t...