We apply a Bayesian hierarchical Poisson spatial interaction model to the paper trail left by patent citations between high-technology patents in Europe to identify and measure spatial separation effects of interregional knowledge flows. The model introduced here is novel in that it allows for spatially structured origin and destination effects for the regions. Estimation of the model is carried out within a Bayesian framework using data augmentation and Markov Chain Monte Carlo (MCMC) methods, related to recent work in Frühwirth-Schnatter and Wagner (2004). This allows MCMC sampling from well-known distribution families, and thus provides a substantial improvement over MCMC estimation based on Metropolis-Hastings sampling from non-standard conditional distributions.
Ziele des Unterlagenzuchtprogrammes waren die Verbesserung der Vermehrbarkeit, Schwachwüchsigkeit, Resistenz gegenüber biotischen und abiotischen
Abstract The process of economic integration within Europe is a highly complex and multi-faceted phenomenon. There is little doubt, however, that the European Community has made persistent efforts to encourage the widening (territorial enlargement) and deepening (increasing the integration of existing member countries) of the integration process. This was true of the first territorial enlargement in the 1970s, and is even more so in the 1980s and 1990s, when the Community decided, ambitiously, to enlarge and deepen at the same time.
In many Floras, keys rarely reach acceptable scientific standards. Terminology, clarity, coherence, accuracy, style and diction all leave much to be desired and have to improve so that Floras can achieve and match set botanical standards. They should not deter potential users within and outside the realm of botany. Examples of illogical and unpractical keys in some European Floras are provided. The reasons for the existence and preparation of unsuitable keys are discussed; they are mainly based on two different approaches - the holistic outlook adopted by floristic workers and the analytical attitude of scientists.
This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step involves identifying the number and composition of clubs using a space-time panel data model for annual income growth rates in conjunction with Bayesian model comparison methods. A second step uses a Bayesian space-time panel data model to assess how changes in the initial endowments of variables (that explain growth) impact regional income levels over time. These dynamic trajectories of changes in regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in the initial conditions for the clubs that have been identified in the first step. This is in contrast to conventional practice that involves setting the number of clubs ex ante, selecting the composition of the potential convergence clubs according to some a priori criterion (such as initial per capita income thresholds for example), and using cross-sectional growth regressions for estimation and interpretation purposes. KEYWORDS: Dynamic space-time panel data model, Bayesian model comparison, European regions JEL Classification: C11, C23, O47, O52
A general framework to analyze communication media choice behavior in the university setting is proposed which integrates a stated preference experimental design procedure into a discrete choice modeling framework. The framework is empirically tested using hypothetical choice experiments in which traditional mail, courier mail, telephone, facsimile, and electronic mail services were choice options to carry out information communication tasks. For this purpose face-to-face interviews were conducted in six universities in Austria and Switzerland. The choice modeling approach developed emphasizes the influence of communication context specific characteristics, individual and organizational characteristics of the communication initiator as well as the individual's perceptions and feelings about the communication media on the formation of preferences. Empirical results are presented using stated preference models of communication media choice behavior for a series of communication situations. Specific emphasis is laid on crossnational differences in choice behavior.
No abstract is provided for this article.
The focus in this paper is on knowledge spillovers between hightechnology firms in Europe, as captured by patent citations. The European coverage is given by patent applications at the European Patent Office (EPO) that are assigned to high-technology firms located in Europe. By following the paper trail left by citations between high-technology patents we adopt a case–control matching approach to test the extent of localisation of knowledge spillovers at two geographic levels, the region and the country level. This approach views a finding of disproportionate co-location of patent citations relative to co-located control patents as evidence of localised knowledge spillovers. To disentangle border from geographic distance effects the paper adopts a Poisson spatial interaction modelling perspective. The findings of the study not only indicate that localisation of knowledge spillovers exists, but also that national border effects are more important than geographical distance effects. Thus, knowledge flows within European countries more easily than across. Not only geography matters, but also technological proximity. Interregional knowledge flows are industry specific and occur most often between regions located close to each other in technological space.
Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to exp
No abstract is provided for this article.
This paper suggests an empirical framework for analysing income distribution dynamics and cross-region convergence in the European Union of 27 member states, 1995-2003. The framework lies in the research tradition that allows the state income space to be continuous, puts emphasis on both shape and intra-distribution dynamics and uses stochastic kernels for studying transition dynamics and implied long-run behaviour. In this paper stochastic kernels are described by conditional density functions, estimated by a product kernel estimator of conditional density and represented by means of novel visualisation tools. The technique of spatial filtering is used to account for spatial effects, in order to avoid misguided inferences and interpretations caused by the presence of spatial autocorrelation in the income distributions. The results reveal a slow catching-up of the poorest regions and a process of polarisation, with a small group of very rich regions shifting away from the rest of the cross-section. This is well evidenced by both, the unfiltered and the filtered ergodic density view. Differences exist in detail, and these emphasise the importance to properly deal with the spatial autocorrelation problem.
It is well-known that large neural networks with many unshared weights can be very difficult to train. A neural network ensemble consisting of a number of individual neural networks usually performs better than a complex monolithic neural network. One of the motivations behind neural network ensembles is the divide-and-conquer strategy, where a complex problem is decomposed into different components each of which is tackled by an individual neural network. A promising algorithm for training neural network ensembles is the negative correlation learning algorithm which penalizes positive correlations among individual networks by introducing a penalty term in the error function. A penalty coefficient is used to balance the minimization of the error and the minimization of the correlation. It is often very difficult to select an optimal penalty coefficient for a given problem because as yet there is no systematic method available for setting the parameter. This paper first applies negative correlation learning to the traffic flow prediction problem, and then proposes an evolutionary approach to deciding the penalty coefficient automatically in negative correlation learning. Experimental results on the traffic flow prediction problem will be presented.
Comparable patient populations with 160 uncoated RM acetabular cups and 263 cemented Müller standard acetabular cups were submitted to survivaltime an
