Von mises calculus for statistical functions

Quadratic semiparametric Von Mises calculus.

We discuss a new method of estimation of parameters in semiparametric and nonparametric models. The method is based on U-statistics constructed from quadratic influence functions. The latter extend ordinary linear influence functions of the parameter of interest as defined in semiparametric theory, and represent second order derivatives of this parameter. For parameters for which the matching c...

Approximations for Von Mises

We prove Edgeworth expansions for degenerate von Mises statistics like the Beran, Watson, and Cram er-von Mises goodness-of-t statistics. Furthermore, we show that the bootstrap approximation works up to an error of order O(N ?1=2) and that Bootstrap based conndence regions attain a prescribed conndence level up to the order O(n ?1).

Performance Analysis of VON MISES’ Motor Calculus within Modelica

This paper presents an alternative concept of modelling multibody systems within Modelica, the socalled motor calculus. This approach was introduced by R. VON MISES in 1924 and can be used to describe the dynamical behaviour of spatial multibody systems in a very efficient way. While the equations clearly take a very simple form in terms of motor algebra, the numerical efficiency is still an op...

Almost Sure Behaviour of U - Statistics and Von Mises ' Differentiable Statistical Functions

Cramér-von Mises regression

Consider a linear regression model with unknown regression parameters 0 and independent errors of unknown distribution. Block the observations into q groups whose independent variables have a common value and measure the homogeneity of the blocks of residuals by a Cramér-von Mises q-sample statistic Tq( ). This statistic is designed so that its expected value as a function of the chosen regress...