The Persistence of Neighboring as a Determinant of Community Attachment: A Community Field Perspective*

We examined the community field perspective as a complement to the linear‐development and systemic models of community attachment, wherein community attachment is defined as a social bond to the community of place. We empirically evaluated indicators of the actor's interaction within the social field, such as the perceived quality of neighboring and density of friendships, using social survey and census data from 99 communities in 1994 and 2004 and evaluated as well the percentage change in these indicators from 1994 to 2004. Results show that the community field perspective can complement previous approaches by highlighting the importance of perceived neighboring and friendship density and the persistence of perceived neighboring over time. We suggest implications for community development efforts aimed at enhancing community attachment.     

Hierarchische Planung für industrielle Logistikdienstleister

In this paper a planning and control system for logistics service providers (called LPS system) is developed under consideration of their specific flexibility potentials. Due to the complexity of such planning decisions (different planning levels and data quality of these levels as well as time horizons etc.), a multi-stage, hierarchical planning system is proposed which minimizes the relevant costs under consideration of the flexibilities. The advantage of a hierarchical planning concept consists in a limited planning complexity because otherwise data collection and solution development would lead to unsolvable problems in practice. The results of using LPS systems for a real-life case show a significant pay-off for logistics service providers due to the remarkable cost savings by applying a hierarchical planning concept.     

A study of Bayesian local robustness with applications in actuarial statistics

Local or infinitesimal Bayesian robustness is a powerful tool to study the sensitivity of posterior magnitudes, which cannot be expressed in a simple manner. For these expressions, the global Bayesian robustness methodology does not seem adequate since the practitioner cannot avoid using inappropriate classes of prior distributions in order to make the model mathematically tractable. This situation occurs, for example, when we compute some types of premiums in actuarial statistics in order to fix the premium to be charged to an insurance policy. In this paper, analytical and simple expressions that allow us to study the sensitivity of premiums, which are usually used in automobile insurance are provided by using the local Bayesian robustness methodology. Some examples are examined by using real automobile claim insurance data.     

A perspective of composite sampling

Composite samples are formed by physically mixing samples. Usually, composite samples are used to reduce the overall cost associated with analytical procedures that must be performed on each sample, but they can also be used to protect the privacy of individuals.

Composite sampling can reduce the cost of identifying individual cases that have a certain trait, such as those with a rare disease or those exceeding pollution-level standards. Not much is lost by applying this method as long as the trait is relatively rare.

Composite sampling can reduce the cost of estimating the mean of some process. When samples are composited, the ability to estimate the variance is lost. In spite of this, the potential savings are so great that composite samples have been used.

Much of this paper deasl with the variance of estimators based on composite sampling when the porportions of hte original samples comprising the composite sample are actually random. Taking repeated samples and measurements on several composite samples complicates the prodcedure, but allows the estimation of between and within variation as well as measurement error.     

Computational algorithms for the factor model

Algorithms for computing the maximum likelihood estimators and the estimated covariance matrix of the estimators of the factor model are derived. The algorithms are particularly suitable for large matrices and for samples that give zero estimates of some error variances. A method of constructing estimators for reduced models is presented. The algorithms can also be used for the multivariate errors-in-variables model with known error covariance matrix.     

Alternative Methods for Computing the Sensitivity of Complex Surveillance Systems

Stochastic scenario trees are a new and popular method by which surveillance systems can be analyzed to demonstrate freedom from pests and disease. For multiple component systems—such as a combination of a serological survey and systematically collected observations—it can be difficult to represent the complete system in a tree because many branches are required to represent complex conditional relationships. Here we show that many of the branches of some scenario trees have identical outcomes and are therefore redundant. We demonstrate how to prune branches and derive compact representations of scenario trees using matrix algebra and Bayesian belief networks. The Bayesian network representation is particularly useful for calculation and exposition. It therefore provides a firm basis for arguing disease freedom in international forums.     

Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions

The main goal in this paper is to develop and apply stochastic simulation techniques for GARCH models with multivariate skewed distributions using the Bayesian approach. Both parameter estimation and model comparison are not trivial tasks and several approximate and computationally intensive methods (Markov chain Monte Carlo) will be used to this end. We consider a flexible class of multivariate distributions which can model both skewness and heavy tails. Also, we do not fix tail behaviour when dealing with fat tail distributions but leave it subject to inference.     

Study of the behaviour of flexible production systems using neural nets

Owing to high development and acquisition costs, production systems require very detailed analysis for pre-design specifications, and an adjustment in operating philosophies. Detailed simulation models are one of the most important instruments to achieve this goal; normally the simulator must be custom-built, based on an accurate study of the specific industrial problem. The proposed management and economical analysis uses mathematical meta-models obtained from experimental designs produced by the simulator; today the detail level and precision required is very high, so these kinds of meta-model operate very close to their limit. Here we present a new methodology based on artificial neural networks that produces new types of meta-models which can correctly represent complex industrial systems. In this work we propose an application of this methodology together with a detailed analysis of the model development phase to highlight the potential of this instrument.