Die Einführung von Mitarbeiter/innengesprächen an österreichischen Universitäten

The introduction of staff appraisal interviews in Austrian Universities This paper addresses the issue of the obligatory introduction of staff appraisal interviews in the Austrian higher education system after the implementation the law ?UG 2002“ (University Law 2002). It focuses on the cultural change process from the old university management system to a nowadays prevalent ?management by objectives“. After the introduction of the ?UG 2002“ Austrian Universities are forced to be more competitive with one another, hence an academic market is opened. Ultimately this leads to a cultural break. This is the reason why we discuss the possibilities and limitations of the implementation of appraisal interviews and target agreements from different angles. The diverse perspectives will be a theory of communication, a theory of organizations and a psychodynamic standpoint.     

Two tests for sequential detection of a change-point in a nonlinear model

In this paper, two tests, based on weighted CUSUM of the least squares residuals, are studied to detect in real time a change-point in a nonlinear model. A first test statistic is proposed by extension of a method already used in the literature but for the linear models. It is tested under the null hypothesis, at each sequential observation, that there is no change in the model against a change presence. The asymptotic distribution of the test statistic under the null hypothesis is given and its convergence in probability to infinity is proved when a change occurs. These results will allow to build an asymptotic critical region. Next, in order to decrease the type I error probability, a bootstrapped critical value is proposed and a modified test is studied in a similar way. A generalization of the Hájek–Rényi inequality is established.     

On optimising the estimation of high quantiles of a probability distribution

One of the major aims of one-dimensional extreme-value theory is to estimate quantiles outside the sample or at the boundary of the sample. The underlying idea of any method to do this is to estimate a quantile well inside the sample but near the boundary and then to shift it somehow to the right place. The choice of this “anchor quantile” plays a major role in the accuracy of the method. We present a bootstrap method to achieve the optimal choice of sample fraction in the estimation of either high quantile or endpoint estimation which extends earlier results by Hall and Weissman (1997) in the case of high quantile estimation. We give detailed results for the estimators used by Dekkers et al. (1989). An alternative way of attacking problems like this one is given in a paper by Drees and Kaufmann (1998).     

The S-estimator in the change-point random model with long memory

This paper considers two-phase random design linear regression models. Errors and regressors are stationary long-range-dependent Gaussian processes. The regression parameters, the scale parameter and the change-point are estimated using a method introduced by Rousseeuw and Yohai [Robust regression by means of S-estimators, in Robust and Nonlinear Time Series Analysis, J. Franke, W. Hrdle, and R.D. Martin, eds., Lecture Notes in Statistics, Vol. 26, Springer, New York, 1984, pp. 256–272], which is called the S-estimator and has the property be more robust than the classical estimators in the sense that the outliers do not bias the estimation results. Some asymptotic results, including the strong consistency and the convergence rate of the S-estimator are proved. Simulations and an application to the Nile River data are also presented. It is shown via Monte Carlo simulations that the S-estimator is better than two other estimators that are proposed in the literature.     

A Comparison of Hierarchical Methods for Clustering Functional Data

Functional data analysis (FDA)—the analysis of data that can be considered a set of observed continuous functions—is an increasingly common class of statistical analysis. One of the most widely used FDA methods is the cluster analysis of functional data; however, little work has been done to compare the performance of clustering methods on functional data. In this article, a simulation study compares the performance of four major hierarchical methods for clustering functional data. The simulated data varied in three ways: the nature of the signal functions (periodic, non periodic, or mixed), the amount of noise added to the signal functions, and the pattern of the true cluster sizes. The Rand index was used to compare the performance of each clustering method. As a secondary goal, clustering methods were also compared when the number of clusters has been misspecified. To illustrate the results, a real set of functional data was clustered where the true clustering structure is believed to be known. Comparing the clustering methods for the real data set confirmed the findings of the simulation. This study yields concrete suggestions to future researchers to determine the best method for clustering their functional data.     

Hayter and Tsui's test with double sampling for the vector mean of multivariate normal population

Abstract

In this paper, we introduce a version of Hayter and Tsui's statistical test with double sampling for the vector mean of a population under multivariate normal assumption. A study showed that this new test was more or as efficient than the well-known Hotelling's T² with double sampling. Some nice features of Hayter and Tsui's test are its simplicity of implementation and its capability of identifying the errant variables when the null hypothesis is rejected. Taking that into consideration, a new control chart called HTDS is also introduced as a tool to monitor multivariate process vector mean when using double sampling.     

Quadratic estimators of covariance components in a multivariate mixed linear model

It is known that the Henderson Method III (Biometrics 9:226–252, 1953) is of special interest for the mixed linear models because the estimators of the variance components are unaffected by the parameters of the fixed factor (or factors). This article deals with generalizations and minor extensions of the results obtained for the univariate linear models. A MANOVA mixed model is presented in a convenient form and the covariance components estimators are given on finite dimensional linear spaces. The results use both the usual parametric representations and the coordinate-free approach of Kruskal (Ann Math Statist 39:70–75, 1968) and Eaton (Ann Math Statist 41:528–538, 1970). The normal equations are generalized and it is given a necessary and sufficient condition for the existence of quadratic unbiased estimators for covariance components in the considered model.     

Time to reconceptualize ‘green technology’ in the context of globalization and international relations

The focus on time is used to demonstrate that Newtonian conceptions of reality underpin the push for ‘green technology’ and its associated international relations. It brings to the surface the rationale of this technology and shows its limits and future potential by exploring underlying assumptions and principles: ‘green technology’, it is argued, is not achievable on those terms and such an approach to environmental problems is unlikely to lead to relevant action and suitable solutions. It is not simply a matter of finding the ‘right technical, economic and political solutions but, crucially, about reconceptualization and the restructuring of knowledge. Thus, conceptual revision is necessary if appropriate responses are to be found to the hazards of the industrial way of life; focus on the temporal dimension of technology and globalized political processes offers a first point of departure from the Newtonian tradition.     

Building a new international is necessary and urgent

ABSTRACT

Samir Amin was one of the most creative Marxists, but also an orthodox, as his appeal for a new international proves, consistent with his defense of the socialist revolution against a decaying capitalist system, which threatens with the extinction of human civilization. However, in a contradictory way, if in the past the material conditions did not exist, they exist today, materializing in a distorted manner by capitalism, the organization of a revolutionary international leadership has never been so non-existent. This article begins by demonstrating the systemic conditions that require the proletariat to build a new International, the reasons for the current difficulty in achieving it; and, from the analysis of the most successful experience to date, the Third International, lessons are drawn for a future Fifth International.