Clean technologies in German economic literature: a bibliometric analysis

Over the past two decades, clean technologies (cleantech) have emerged as an important economic factor with remarkable progress. Fueled by growing concerns about climate change and diminishing fossil fuel resources, governments have put aggressive stimulus packages in place to support emerging technologies that drive cleantech businesses. The industry operates in highly regulated market conditions which in turn raises the question of whether economists have addressed private and public information requirements. To answer this question and to explore the relationship between environmental economics and clean technologies, this paper examines German literature using co-citation analysis methods. Based on the co-citation analysis of 588 documents, our results suggest that researchers have provided a rather small and fragmented set of business knowledge for the cleantech industry. Despite its economic and environmental importance, research on the private use and economic impact of cleantech remains scarce.     

A tutorial on spectral clustering

In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.     

Repeat and Non-returning Volunteers: The Promise of Episodic Events for Volunteer Recruitment and Retention

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - In this article, we are concerned with the recruitment potential of one-off episodic events for attracting and retaining...     

Gewalt als Reaktion auf Anerkennungsdefizite?

Violence between adolescents is primarily a male phenomenon. For this reason, the analysis of violent behavior focuses on 4.213 male adolescents from three different backgrounds: adolescents of Turkish descent, late migrants from Russia and Poland (Aussiedler) and native Germans. They all are attending schools (10th grade) in Northrhine-Westfalia, Germany. The data have been provided by the Youth-Survey 2001 of the Institute for Interdisciplinary Research on Conflict and Violence (IKG). The theoretical framework is provided by the theory of disintegration developed at the IKG. In comparison to Germans, Turkish adolescents show a higher rate of violence. The bivariate effect of Turkish descent on violent behavior can in multivariate logistic regression analysis partly be explained by a lack of recognition at the institutional and at the socio-structural dimensions of integration: In comparison to Germans, Turkish adolescents experience more discrimination, both in their daily life and in their school career. Those German adolescents and late migrants who suffer from a comparable degree of discrimination show similar patterns of violent behavior. Lack of recognition at the socio-emotional dimension (i.e. the relationship with the parents) have a smaller effect on violent behavior. In sum, especially the accumulation of deficits at different dimensions of integration in Germany heightens the risk for violent action.     

Comparing Experimental Designs for Benchmark Dose Calculations for Continuous Endpoints

The BMD (benchmark dose) method that is used in risk assessment of chemical compounds was introduced by Crump (1984) and is based on dose-response modeling. To take uncertainty in the data and model fitting into account, the lower confidence bound of the BMD estimate (BMDL) is suggested to be used as a point of departure in health risk assessments. In this article, we study how to design optimum experiments for applying the BMD method for continuous data. We exemplify our approach by considering the class of Hill models. The main aim is to study whether an increased number of dose groups and at the same time a decreased number of animals in each dose group improves conditions for estimating the benchmark dose. Since Hill models are nonlinear, the optimum design depends on the values of the unknown parameters. That is why we consider Bayesian designs and assume that the parameter vector has a prior distribution. A natural design criterion is to minimize the expected variance of the BMD estimator. We present an example where we calculate the value of the design criterion for several designs and try to find out how the number of dose groups, the number of animals in the dose groups, and the choice of doses affects this value for different Hill curves. It follows from our calculations that to avoid the risk of unfavorable dose placements, it is good to use designs with more than four dose groups. We can also conclude that any additional information about the expected dose-response curve, e.g., information obtained from studies made in the past, should be taken into account when planning a study because it can improve the design.     

Tests for high-dimensional covariance matrices using the theory of U-statistics

Test statistics for sphericity and identity of the covariance matrix are presented, when the data are multivariate normal and the dimension, p, can exceed the sample size, n. Under certain mild conditions mainly on the traces of the unknown covariance matrix, and using the asymptotic theory of U-statistics, the test statistics are shown to follow an approximate normal distribution for large p, also when p?n. The accuracy of the statistics is shown through simulation results, particularly emphasizing the case when p can be much larger than n. A real data set is used to illustrate the application of the proposed test statistics.     

A spatial model for the needle losses of pine-trees in the forests of Baden-Württemberg: an application of Bayesian structured additive regression

Summary.  The data that are analysed are from a monitoring survey which was carried out in 1994 in the forests of Baden-Württemberg, a federal state in the south-western region of Germany. The survey is part of a large monitoring scheme that has been carried out since the 1980s at different spatial and temporal resolutions to observe the increase in forest damage. One indicator for tree vitality is tree defoliation, which is mainly caused by intrinsic factors, age and stand conditions, but also by biotic (e.g. insects) and abiotic stresses (e.g. industrial emissions). In the survey, needle loss of pine-trees and many potential covariates are recorded at about 580 grid points of a 4 km × 4 km grid. The aim is to identify a set of predictors for needle loss and to investigate the relationships between the needle loss and the predictors. The response variable needle loss is recorded as a percentage in 5% steps estimated by eye using binoculars and categorized into healthy trees (10% or less), intermediate trees (10–25%) and damaged trees (25% or more). We use a Bayesian cumulative threshold model with non-linear functions of continuous variables and a random effect for spatial heterogeneity. For both the non-linear functions and the spatial random effect we use Bayesian versions of P -splines as priors. Our method is novel in that it deals with several non-standard data requirements: the ordinal response variable (the categorized version of needle loss), non-linear effects of covariates, spatial heterogeneity and prediction with missing covariates. The model is a special case of models with a geoadditive or more generally structured additive predictor. Inference can be based on Markov chain Monte Carlo techniques or mixed model technology.