Shift scheduling for tank trucks

In this paper we deal with shift scheduling of tank trucks for a small oil company. Given are a set of tank trucks with different characteristics and a set of drivers with different skills. The objective is to assign a feasible driver to every shift of the tank trucks such that legal and safety restrictions are satisfied, the total working times of the drivers are within desired intervals, requested vacation of the drivers is respected and the trucks are assigned to more favored drivers. We propose a two-phase solution algorithm which is based on a mixed integer linear programming formulation and an improvement procedure. Computational results are reported showing that the algorithm is able to generate feasible schedules in a small amount of time.     

The non-central negative binomial distribution: Further properties and applications

Abstract

The non-central negative binomial distribution is both a mixed and compound Poisson distribution with applications in photon and neural counting, statistical optics, astronomy and a stochastic reversible counter system. In this paper various important probabilistic properties of the non-central negative binomial distribution in practical applications like log-concavity, discrete self-decomposability, unimodality, asymptotic behavior and tail length of the probability distribution have been derived. The construction as a mixed Poisson process by specifying a joint distribution for the inter-arrival times and its application is illustrated by a fit to real life data set.     

Estimation of the Lognormal-Pareto Distribution Using Probability Weighted Moments and Maximum Likelihood

This article deals with the estimation of the lognormal-Pareto and the lognormal-generalized Pareto distributions, for which a general result concerning asymptotic optimality of maximum likelihood estimation cannot be proved. We develop a method based on probability weighted moments, showing that it can be applied straightforwardly to the first distribution only. In the lognormal-generalized Pareto case, we propose a mixed approach combining maximum likelihood and probability weighted moments. Extensive simulations analyze the relative efficiencies of the methods in various setups. Finally, the techniques are applied to two real datasets in the actuarial and operational risk management fields.     

You're all I need to get by? Analyzing young entrepreneurs’ networks in Morocco from a “quantified narratives” method

The embeddedness of entrepreneurs in a network of social relations has been largely documented by the literature since the seminal work of Granovetter. In different contexts, it has been shown that the entrepreneurial network plays a central role in the creation of their business and provides a variety of resources. Few studies, however, seek to understand the motivations of entrepreneurs when they mobilize a tie, and the effects of these mobilizations on the content and quality of this relationship. From a fieldwork undertaken in Morocco, we implement a “quantified narratives” method which codes each sequence of access to a resource in the course of the start-up in order to map the network of relationships of young innovative entrepreneurs. We go further than previous studies using this method in that we develop a multivariate model that enables us to build hypotheses from qualitative material analysis and to test them econometrically. This case study highlights more generally the interest of mixed methods in analyzing complex mechanisms like network mobilization and their motivations. We analyze the different logics which led entrepreneurs to the mobilization of the network at the time of the creation of their business. The main result that emerges is that the family is sidelined in obtaining funding. This original result is indicative of the tension faced by these young Moroccan entrepreneurs: the need to grow their business is sometimes in contradiction with the desire to create through it a space of autonomy gained on the family.     

What is your research question? A mixed methods evaluation of an academic statistical consulting center

Academic consulting centers on research and statistics are the bridge between applied researchers and statisticians and thus at the core of university-wide research. The client-centered evaluation focused on investigating the perspective of the clients in university research and statistical consulting center. A mixed-methods methodology was used in this study, specifically a concurrent triangulation design was implemented to have multiple data sources collected and analyzed simultaneously in order to identify areas of overlapping information. The Research Consulting Scale (RCS) instrument was developed and analyzed using an exploratory factor analysis with 129 participants and resulted in two factors: consulting experience, and consulting facilities. The internal consistency reliability of the scores for these two factors was 0.89 and .86 respectively. These results support the RCS has strong internal consistency. Additionally, client interviews were conducted sampling from those who had responded to the survey in order to gather additional data. Thematic analysis was performed to interview data and resulted in two major themes: consultant expertise and consultancy skills. The results provide a survey instrument and key themes for university consulting centers to focus and assess their efficiency through client’s perspectives.     

Income Security During Sickness Absence. What Do British Middle‐classes Do?

Income maintenance during sickness absence is an under‐researched field within social policy analysis, and yet it is conducive to exploring the interplay between statutory, corporate and private forms of income protection. Drawing on original qualitative interview data, the article shows that British middle‐class couples largely ignore or dismiss public provision, which is due to a relatively low level of sickness benefits, but also based on misconceptions about social rights and the role of employers as mandatory (and voluntary) sick pay providers. Despite a considerable degree of mistrust, mortgage‐related private sickness insurance mattered to some extent, although this does not necessarily reflect policyholders’ strategic choices vis à vis current household needs for income security. Other potential sources of income replacement, such as savings, are relied on much less. In general, the analysis shows a heavy middle‐class reliance on, and strong confidence in, employer‐based sickness pay. This finding may be contrasted with questions about the sustainability of voluntary corporate provision, as well as its capacity to provide income security for the workforce as a whole.     

A mixed membership model-based measure for subgroup integration in social networks

Social networks analysis often involves quantifying subgroup structure in which tie density is greater among nodes in the same subgroup than between subgroups. One such measure, subgroup insularity or segregation, is the extent that subgroups are separate from each other. We introduce a new measure, γ, which is a parameter from the mixed membership stochastic blockmodel (MMSBM; Airoldi et al., 2008), and differs from many existing measures in that γ does not depend on node membership. We compare this measure to several well-known measures and use simulation studies and real data analysis to provide insight into how this measure can be used in practice.     

A self-normalizing approach to the specification test of mixed-frequency models

In econometrics and finance, variables are collected at different frequencies. One straightforward regression model is to aggregate the higher frequency variable to match the lower frequency with a fixed weight function. However, aggregation with fixed weight functions may overlook useful information in the higher frequency variable. On the other hand, keeping all higher frequencies may result in overly complicated models. In literature, mixed data sampling (MIDAS) regression models have been proposed to balance between the two. In this article, a new model specification test is proposed that can help decide between the simple aggregation and the MIDAS model.     

Modelling price paths in on-line auctions: smoothing sparse and unevenly sampled curves by using semiparametric mixed models

Summary.  On-line auctions pose many challenges for the empirical researcher, one of which is the effective and reliable modelling of price paths. We propose a novel way of modelling price paths in eBay's on-line auctions by using functional data analysis. One of the practical challenges is that the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data, which can be difficult if the data are irregularly distributed. We present a new approach that can overcome this challenge. The approach is based on the ideas of mixed models. Specifically, we propose a semiparametric mixed model with boosting to recover the functional object. As well as being able to handle sparse and unevenly distributed data, the model also results in conceptually more meaningful functional objects. In particular, we motivate our method within the framework of eBay's on-line auctions. On-line auctions produce monotonic increasing price curves that are often correlated across auctions. The semiparametric mixed model accounts for this correlation in a parsimonious way. It also manages to capture the underlying monotonic trend in the data without imposing model constraints. Our application shows that the resulting functional objects are conceptually more appealing. Moreover, when used to forecast the outcome of an on-line auction, our approach also results in more accurate price predictions compared with standard approaches. We illustrate our model on a set of 183 closed auctions for Palm M515 personal digital assistants.