Pricing of Full‐Service Repair Contracts with Learning,Optimized Maintenance,and Information Asymmetry

This article considers the optimal pricing of full‐service (FS) repair contracts by taking into account learning and maintenance efficiency effects, competition from service , and asymmetric information. We analyze on‐call service (OS) and FS contracts in a market where customers exhibit heterogeneous risk aversion. While the customers minimize their disutility over the equipment lifetime, the service provider maximizes expected profits arising from the portfolio of OS and FS contracts. We show that the optimal FS price depends inter alia on the customer's prior cost experience and on OS repair and maintenance costs. The optimal FS price is shown to increase as fewer OS customers are lost to competition, whereas improved repair learning enabled by FS reduces the optimal price. A numerical study based on data from a manufacturer of forklifts highlights the importance of learning in maintenance operations, which constitutes the key benefit of FS contracts; 81% of the customers select the FS option and are willing to pay an insurance premium of around 1.5% of total OS cost against volatility of repair costs.     

Speaking Acquaintances or Helpers in Need: Participation in Civic Associations and Individual Social Capital

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - It is widely believed that civic associations are capable to produce social capital, here understood as an individual...     

On the least-squares model averaging interval estimator

In many applications of linear regression models, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty, least-squares frequentist model averaging has been proposed recently. We show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. The finite-sample confidence intervals based on approximations to the asymptotic distributions are also equivalent if the parameter of interest is a linear function of the regression coefficients. Furthermore, we demonstrate that this equivalence also holds for prediction intervals constructed in the same fashion.     

Type-I-error rate inflation in mixed models for repeated measures caused by ambiguous or incomplete model specifications

Pre-specification of the primary analysis model is a pre-requisite to control the family-wise type-I-error rate (T1E) at the intended level in confirmatory clinical trials. However, mixed models for repeated measures (MMRM) have been shown to be poorly specified in study protocols. The magnitude of a resulting T1E rate inflation is still unknown. This investigation aims to quantify the magnitude of the T1E rate inflation depending on the type and number of unspecified model items as well as different trial characteristics. We simulated a randomized, double-blind, parallel group, phase III clinical trial under the assumption that there is no treatment effect at any time point. The simulated data was analysed using different clusters, each including several MMRMs that are compatible with the imprecise pre-specification of the MMRM. T1E rates for each cluster were estimated. A significant T1E rate inflation could be shown for ambiguous model specifications with a maximum T1E rate of 7.6% [7.1%; 8.1%]. The results show that the magnitude of the T1E rate inflation depends on the type and number of unspecified model items as well as the sample size and allocation ratio. The imprecise specification of nuisance parameters may not lead to a significant T1E rate inflation. However, the results of this simulation study rather underestimate the true T1E rate inflation. In conclusion, imprecise MMRM specifications may lead to a substantial inflation of the T1E rate and can damage the ability to generate confirmatory evidence in pivotal clinical trials.     

Negative situation appraisal and mental well-being among refugees in Germany: Serial mediation by religious coping and sense of coherence

It has been widely demonstrated that pre- and post-migration experiences can be highly traumatic for refugees. The purpose of this study was to assess the mediating effects of religious coping and sense of coherence on the association of negative dysfunctional situation appraisal and mental well-being among refugees. Paper surveys were completed by 600 people representing various refugee groups (mostly from Mashreq Countries) in German Bavaria, aged 18–65. Mediation analysis revealed that negative religious coping and sense of coherence singly and serially mediated the relationship between negative dysfunctional situation appraisal and well-being, whereas positive religious coping did not have statistically significant relationships with these variables. The data obtained suggest that the negative religious coping style and sense of coherence are two important targets for future experimental studies, and interventions on them may bring the synergistic effect on improving mental well-being in refugees.     

Describing a landscape we are yet discovering

AStA Advances in Statistical Analysis -     

Measuring spatial price differentials at the basic heading level: a comparison of stochastic index number methods

AStA Advances in Statistical Analysis - Spatial price comparisons rely to a high degree on the quality of the underlying price data that are collected within or across countries. Below the basic...