Real-time fading channels are affected by multipath fading as well as shadowing. In this thesis, we propose the Inverse-Gaussian distribution as a less complex alternative to the classical Log-normal model to describe shadowing effects in composite multipath fading/shadowing environments and to get closed-form solutions for the most important figures of merit. A main motivation for this selecti...

The inverse Gaussian distribution (IGD) is a well known and often used probability distribution for which fully reliable numerical algorithms have not been available. We develop fast, reliable basic probability functions (dinvgauss, pinvgauss, qinvgauss and rinvgauss) for the IGD that work for all possible parameter values and which achieve close to full machine accuracy. The most challenging t...

We provide a uniformly efficient and simple random variate generator for the entire parameter range of the generalized inverse gaussian distribution. A general algorithm is provided as well that works for all densities that are proportional to a log-concave function φ, even if the normalization constant is not known. It requires only black box access to φ and its derivative.

An important measure of identification risk associated with the release of microdata or large complex tables is the number or proportion of population units that can be uniquely identified by some set of characterizing attributes which partition the population into subpopulations or cells. Various methods for estimating this quantity based on sample data have been proposed in the literature by ...