Control Charts for Poisson Counts based on the Stein-Chen Identity

If monitoring Poisson count data for a possible mean shift (while the Poisson distribution is preserved), then the ordinary Poisson exponentially weighted moving-average (EWMA) control chart proved to be a good solution. In practice, however, mean shifts might occur in combination with further changes in the distribution family. Or due to a misspecification during Phase-I analysis, the Poisson assumption might not be appropriate at all. In such cases, the ordinary EWMA chart might not perform satisfactorily. Therefore, two novel classes of generalized EWMA charts are proposed, which utilize the so-called Stein-Chen identity and are thus sensitive to further distributional changes than just sole mean shifts. Their average run length (ARL) performance is investigated with simulations, where it becomes clear that especially the class of so-called "ABC-EWMA charts" shows an appealing ARL performance. The practical application of the novel Stein-Chen EWMA charts is illustrated with an application to count data from semiconductor manufacturing

Measures of Dispersion and Serial Dependence in Categorical Time Series

The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed serial dependence in categorical time series, we consider three types of κ-measures. By analyzing bias properties, it is shown that always one of the κ-measures is related to one of the above-mentioned dispersion measures. For doing statistical inference based on the sample versions of these dispersion and dependence measures, knowledge on their distribution is required. Therefore, we study the asymptotic distributions and bias corrections of the considered dispersion and dependence measures, and we investigate the finite-sample performance of the resulting asymptotic approximations with simulations. The application of the measures is illustrated with real-data examples from politics, economics and biology

Shareholder vs. stakeholder: ökonomische Fragestellungen

Der folgende Beitrag geht der Frage nach, wie die Verteilung von Entscheidungs- und Handlungsrechten in Unternehmen im Rahmen der Corporate Governance ausgestaltet werden kann. Im Zentrum der Überlegungen steht die Frage, welcher der am Unternehmen beteiligten Interessengruppen diese Rechte sinnvollerweise zukommen sollten. Insbesondere die beiden polaren Systeme - das auf dem Shareholder-Value-Primat aufbauende System einer ausschließlich im Interesse der Aktionäre geführten Unternehmung auf der eine Seite - und einem Corporate Governance-System, das die Interessen aller am Unternehmen beteiligten Stakeholder berücksichtigt, auf der anderen Seite - werden geschildert und mit den Mitteln der ökonomischen Theorie bewertet. Spezifische Investitionen möglicher Stakeholder und die Institutionen und Mechanismen, die eine Absicherung der daraus entstehenden ökonomischen Renten für die jeweiligen Stakeholder erlauben, sind damit wichtige Bestimmungsparameter für die Unternehmensverfassung. Insbesondere die Existenz und Güte von Märkten innerhalb des Finanzsystems, in dem ein Unternehmen tätig ist, lassen das ein oder das andere Corporate Governance-System vorteilhafter erscheinen. Überlegungen zu anderen möglichen Mechanismen, die auf der internen Organisation von Unternehmungen basieren und dadurch eine Feinsteuerung von Entscheidungs- und Handlungsrechten - und der damit verbundenen Machtverteilung zwischen den Interessengruppen im Unternehmen - erlaubt, schließen die Arbeit ab

Conditional-mean Multiplicative Operator Models for Count Time Series

Multiplicative error models (MEMs) are commonly used for real-valued time series, but they cannot be applied to discrete-valued count time series as the involved multiplication would not preserve the integer nature of the data. Thus, the concept of a multiplicative operator for counts is proposed (as well as several specific instances thereof), which are then used to develop a kind of MEMs for count time series (CMEMs). If equipped with a linear conditional mean, the resulting CMEMs are closely related to the class of so-called integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models and might be used as a semi-parametric extension thereof. Important stochastic properties of different types of INGARCH-CMEM as well as relevant estimation approaches are derived, namely types of quasi-maximum likelihood and weighted least squares estimation. The performance and application are demonstrated with simulations as well as with two real-world data examples.Comment: 45 page

The ARGUS Vertex Trigger

A fast second level trigger has been developed for the ARGUS experiment which recognizes tracks originating from the interaction region. The processor compares the hits in the ARGUS Micro Vertex Drift Chamber to 245760 masks stored in random access memories. The masks which are fully defined in three dimensions are able to reject tracks originating in the wall of the narrow beampipe of 10.5\,mm radius.Comment: gzipped Postscript, 27 page