Die Komplexit?t ist mehr in jeder Hinsicht und darf nicht "simplifziert" werden. Das Ziel dieser empirischen Studie ist eine Antwort auf die Fragen zu finden, ob die objektiv-rationale Komplexit?tsbew?ltigung im Multiprojektmanagement an ihre Grenzen st??t und, daraus abgeleitet, wie...

Abstract only: Today’s data analysts and modellers are in the luxurious position of being able to more closely describe, estimate, predict and infer about complex systems of interest, thanks to ever more powerful computational methods but also wider ranges of modelling distributions. Mixture...

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it...

The estimation of the slope parameter of two linear regression models with normal errors are considered, when it is suspected that the two lines are parallel. The uncertain prior information about the equality of slopes is presented by a null hypothesis and a coefficient of distrust on the null...

In Bayesian approach of statistical analyses we incorporate the prior information about the parameter of the model with the observed data. This prior information is in the form of a prior distribution of the parameter. If the prior information is available as a constant value of the parameter...

The estimation of the mean of an univariate normal population with unknown variance is considered when uncertain non-sample prior information is available. Alternative estimators are defined to incorporate both the sample as well as the non-sample information in the estimation process. Some of...

Improved James-Stein type estimation of the mean vector$\mbox{\boldmath $\mu$}$ of a multivariate Student-t population ofdimension p with $\nu$degrees of freedom is considered. In addition to the sample data, uncertain prior information on the value of the mean vector, in the form ofa null...

Log-linear modeling is a popular statistical tool for analysing a contingency table. This presentation focuses on an alternative approach to modeling ordinal categorical data. The technique, based on orthogonal polynomials, provides a much simpler method of model fitting than the conventional...

Over the past few decades correspondence analysis has gained an international reputation as a powerful statistical tool for the graphical analysis of contingency tables. This popularity stems from its development and application in many European countries, especially France, and its use has...

Log-linear modeling is a popular statistical tool for analysing a contingency table. This presentation focuses on an alternative approach to modeling ordinal categorical data. The technique, based on orthogonal polynomials, provides a much simpler method of model fitting than the conventional...