From the known statistics of fibre-fibre contacts in random fibre networks, an analytic estimate is obtained for the variance of local porosity in random fibre suspensions and evolving filtrate networks. The variance of local porosity, and hence the distribution of projected areal density, seems to depend on fibre geometry only through the cube of mean diameter. Also, the coefficient of variati...

Traditional portfolio optimization has been often criticized since it does not account for estimation risk. Theoretical considerations indicate that estimation risk is mainly driven by the parameter uncertainty regarding the expected asset returns rather than their variances and covariances. This is also demonstrated by several numerical studies. The global minimum variance portfolio has been a...

Several techniques, such as adaptive smoothing [9, 10] or anisotropic diffusion [4, 5] deal with the task of local smoothing. That is, preserving principal discontinuities and smoothing within regions. Unfortunately, these types of iterative techniques have as one of their main drawbacks, the determination of the threshold on the luminance gradient. There is no way to control it easily and rese...

We propose alternative discriminant measures for selecting the best basis among a large collection of orthonormal bases for classification purposes. A generalization of the Local Discriminant Basis Algorithm of Saito and Coifman is constructed. The success of these new methods is evaluated and compared to earlier methods in experiments.

We propose and study properties of several estimators of variance decomposition in the local-projections framework. We find for empirically relevant sample sizes that, after being bias corrected with bootstrap, our estimators perform well in simulations. We also illustrate the workings of our estimators empirically for monetary policy and productivity shocks.

It is demonstrated that a set of local orthonormal Hartree-Fock (HF) molecular orbitals can be obtained for both the occupied and virtual orbital spaces by minimizing powers of the orbital variance using the trust-region algorithm. For a power exponent equal to one, the Boys localization function is obtained. For increasing power exponents, the penalty for delocalized orbitals is increased and ...