Supply Chain Conflict Due to Store Brands: The Value of Wholesale Price Commitment in a Retail Supply Chain*

Store brands are of increasing importance in retail supply chains, often causing channel conflict, as the retailer's product directly competes with the manufacturer's national brand. Extant research on the resulting channel interactions either assumes the national brand manufacturer can credibly commit to maintaining a wholesale price or that he lacks such ability. However, these two scenarios imply very different supply chain interactions, as only a national brand manufacturer with commitment ability can strategically adjust a national brand wholesale price to prevent a store brand introduction by the retailer. We specifically analyze the impact of this assumption on the manufacturer, the retailer, and the customers. We determine when long‐term contracts that provide the manufacturer with such commitment ability can improve supply chain profitability.     

Time series models associated with Mittag-Leffler type distributions and its properties

ABSTRACT

We have provided a fractional generalization of the Poisson renewal processes by replacing the first time derivative in the relaxation equation of the survival probability by a fractional derivative of order α(0 < α ? 1). A generalized Laplacian model associated with the Mittag-Leffler distribution is examined. We also discuss some properties of this new model and its relevance to time series. Distribution of gliding sums, regression behaviors, and sample path properties are studied. Finally we introduce the q-Mittag-Leffler process associated with the q-Mittag-Leffler distribution.     

Segmentation,alignment and statistical analysis of biosignals with application to disease classification

We present a novel methodology for a comprehensive statistical analysis of approximately periodic biosignal data. There are two main challenges in such analysis: (1) the automatic extraction (segmentation) of cycles from long, cyclostationary biosignals and (2) the subsequent statistical analysis, which in many cases involves the separation of temporal and amplitude variabilities. The proposed framework provides a principled approach for statistical analysis of such signals, which in turn allows for an efficient cycle segmentation algorithm. This is achieved using a convenient representation of functions called the square-root velocity function (SRVF). The segmented cycles, represented by SRVFs, are temporally aligned using the notion of the Karcher mean, which in turn allows for more efficient statistical summaries of signals. We show the strengths of this method through various disease classification experiments. In the case of myocardial infarction detection and localization, we show that our method compares favorably to methods described in the current literature.     

Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods

ABSTRACT

The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a ‘library’ of candidate prediction models. While SL has been widely studied in a number of settings, it has not been thoroughly evaluated in large electronic healthcare databases that are common in pharmacoepidemiology and comparative effectiveness research. In this study, we applied and evaluated the performance of SL in its ability to predict the propensity score (PS), the conditional probability of treatment assignment given baseline covariates, using three electronic healthcare databases. We considered a library of algorithms that consisted of both nonparametric and parametric models. We also proposed a novel strategy for prediction modeling that combines SL with the high-dimensional propensity score (hdPS) variable selection algorithm. Predictive performance was assessed using three metrics: the negative log-likelihood, area under the curve (AUC), and time complexity. Results showed that the best individual algorithm, in terms of predictive performance, varied across datasets. The SL was able to adapt to the given dataset and optimize predictive performance relative to any individual learner. Combining the SL with the hdPS was the most consistent prediction method and may be promising for PS estimation and prediction modeling in electronic healthcare databases.     

Führungsstile – Evaluation einer Nebenaufgabe im Development Center

To asses six different leadership styles we employed a questionnaire during the development center in a manufacturing firm. Analysing the questionnaire indicates its reliability, scalability, economy, usefulness and fairness. An explorative factor analysis supports the factorial validity. A content analysis provides evidence that the transformational leadership style is perceived as the most positive and the authoritarian leadership style as the most negative one. Furthermore, we found transformational leadership style positively correlated with an aggregated measure for the competence extraversion.     

Parents' experiences with child protection during pregnancy and post-birth

Limited research has directly sought the input of parents involved in the child protection system during pregnancy and with their infants. As the focus of these policies and practices, parents have a unique and important insight not available to others, so it is vital to obtain their input. As part of a larger Australian study, qualitative interviews were undertaken with 13 parents asking about their views and experiences. Parents predominantly became involved with child protection services during pregnancy through a prenatal report. Parents who previously had their newborn removed from their care described it as sudden and unexpected, leaving them distressed and unsupported post-removal, with a growing list of requirements for them to see their baby or for restoration to be considered. Domestic violence was a particular issue of concern for some mothers who expressed distress that their partners, perpetrators of violence, were allowed access to their infant with fewer requirements than for them. Improvements recommended by the parents included greater communication and preparation for the removal, better recognition of improvements in their situations and increased supports to be provided to parents both pre- and post-removal. Parental experiences provide an important guide to improving child protection practice with these families.     

Joint measurement of risk aversion,prudence, and temperance

Risk aversion—but also the higher-order risk preferences of prudence and temperance—are fundamental concepts in the study of economic decision making. We propose a method to jointly measure the intensity of risk aversion, prudence, and temperance. Our theoretical approach is to define risk compensations of different orders, and in an experiment we elicit these compensations with a price list technique. We find evidence for risk aversion, prudence, and temperance. These traits correlate within subjects. The compensations elicited for prudence are significantly larger than those for risk aversion and temperance. In contrast to commonly used utility functions, prospect theory can predict this behavioral pattern. In our experiment, risk-averse, risk-loving, and risk-neutral subjects are prudent. This supports a recent theoretical observation that prudence may be a more universal trait than previously realized.     

Seeking Closure: Competition in Complementary Markets*

When offering a product that has a complementary product in a different market, a firm must consider the interdependence between the complementary products as well as the competition within markets. If the firm participates in both markets, the balancing act becomes even more challenging. This article provides insights about strategies in this latter setting: when should the firm seek to keep its products closed to competing complementary products, and when would the firm be better off by accepting a common standard? To address these questions, we employ standard game theoretic analysis to a simple spatial model that captures aspects of both intermarket externalities and intramarket competition. We find that if a firm participates in both markets and chooses a closed standard, it achieves lower profits compared to an open standard, but gains greater market share. Surprisingly, we find that customers are better off when standards are kept closed.