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.     

Inference on effect size indices from several two-armed experiments

The statistical inference problem on effect size indices is addressed using a series of independent two-armed experiments from k arbitrary populations. The effect size parameter simply quantifies the difference between two groups. It is a meaningful index to be used when data are measured on different scales. In the context of bivariate statistical models, we define estimators of the effect size indices and propose large sample testing procedures to test the homogeneity of these indices. The null and non-null distributions of the proposed testing procedures are derived and their performance is evaluated via Monte Carlo simulation. Further, three types of interval estimation of the proposed indices are considered for both combined and uncombined data. Lower and upper confidence limits for the actual effect size indices are obtained and compared via bootstrapping. It is found that the length of the intervals based on the combined effect size estimator are almost half the length of the intervals based on the uncombined effect size estimators. Finally, we illustrate the proposed procedures for hypothesis testing and interval estimation using a real data set.     

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.     

Pooled Cross-Sectional and Time Series Data: A Survey of Current Statistical Methodology

A data base that provides a multivariate statistical history for each of a number of individual entities is called a pooled cross-sectional and time series data base in the econometrics literature. In marketing and survey literature the terms panel data or longitudinal data are often used. In management science a convenient term might be management data base. Such a data base provides a particularly rich environment for statistical analysis. This article reviews methods for estimating multivariate relationships particular to each individual entity and for summarizing these relationships for a number of individuals. Inference to a larger population when the data base is viewed as a sample is also considered.     

Understanding Canonical Correlation through the General Linear Model and Principal Components

Canonical correlation has been little used and little understood, even by otherwise sophisticated analysts. An alternative approach to canonical correlation, based on a general linear multivariate model, is presented. Properties of principal component analysis are used to help explain the method. Standard computational methods for full rank canonical correlation, techniques for canonical correlation on component scores, and canonical correlation with less than full rank are discussed. They are seen to be essentially equivalent when the model equation for canonical correlation on component scores is presented. The two approaches to less than full rank situations are equivalent in some senses, but quite different in usefulness, depending on the application. An example dataset is analyzed in detail to help demonstrate the conclusions.     

RELIGIOUS REFERENCE GROUPS AND THE PERSISTENCE OF NORMATIVE BEHAVIOR: AN EMPIRICAL TEST

Reference groups and significant others are vitally important in both the formation and the persistence or change of normative as well as deviant behavior patterns. Thus one's initial religious beliefs and behavior (or lack thereof) reflect the socializing influence of the family. However, the situation may change when young people leave home for education or work, as demonstrated by research that shows decreases in religious beliefs or church attendance when young people leave home to attend college. In contrast to the pattern whereby religiosity declines in a college or university environment, we maintain that students who develop close ties with others who are religious, especially in a highly religious community, will maintain the same patterns of high commitment developed in their families. Specifically, we hypothesize that religious beliefs and participation will be positively related to (1) parents' religious beliefs and practices and (2) current friends' religious beliefs and participation. These hypotheses were tested with a sample of college students living on campus (n = 339). The data support the argument that students' current religious beliefs and behavior are related to both their parents' religiosity and the reinforcing effects of the religiosity of their current friends.     

Predictors of college-student food security and fruit and vegetable intake differ by housing type

ABSTRACT

Objective: We assessed whether college-student characteristics associate with food security and fruit and vegetable (FV) intake and whether these associations differ in students in housing with and without food provision. Participants: 514 randomly-sampled students from a large, Midwestern, public university in 2012 and 2013 Methods: Ordered logistic regression tested how student characteristics associate with food security. Linear regression tested how student characteristics associate with FV intake. Analyses were stratified by housing type – that is, housing with food provision (dormitory, fraternity/sorority house, cooperative) vs. housing without food provision.Results: Only among those living in housing without food provision, males (p = 0.04), students without car access (p = 0.005), and those with marginal (p = 0.001) or low (p = 0.001) food security demonstrated lower FV intake. Conclusions: Housing with food provision may buffer the effects of student characteristics on food.     

Confronting Anxiety in Couple and Family Therapy Supervision: A Developmental Supervisory Model Based on Attachment Theory

Referencing relevant supervisory literature and attachment theory, this article presents a developmental couple and family therapy supervisory model that emphasises the efficacy of the supervisory relationship. Issues concerning anxiety, cognition and learning theory are addressed and phases in the supervisory process are identified and described. Cognitive, emotional and social development are linked to attachment theory and discussed in the supervisory context.