$$D_s$$-optimality in copula models

Optimum experimental design theory has recently been extended for parameter estimation in copula models. The use of these models allows one to gain in flexibility by considering the model parameter set split into marginal and dependence parameters. However, this separation also leads to the natural issue of estimating only a subset of all model parameters. In this work, we treat this problem with the application of the \(D_s\)-optimality to copula models. First, we provide an extension of the corresponding equivalence theory. Then, we analyze a wide range of flexible copula models to highlight the usefulness of \(D_s\)-optimality in many possible scenarios. Finally, we discuss how the usage of the introduced design criterion also relates to the more general issue of copula selection and optimal design for model discrimination.     

Development and Validation of a Multidimensional Measure of Family Supportive Supervisor Behaviors (FSSB)

Due to growing work-family demands, supervisors need to effectively exhibit family supportive supervisor behaviors (FSSB). Drawing on social support theory and using data from two samples of lower wage workers, the authors develop and validate a measure of FSSB, defined as behaviors exhibited by supervisors that are supportive of families. FSSB is conceptualized as a multidimensional superordinate construct with four subordinate dimensions: emotional support, instrumental support, role modeling behaviors, and creative work-family management. Results from multilevel confirmatory factor analyses and multilevel regression analyses provide evidence of construct, criterion-related, and incremental validity. The authors found FSSB to be significantly related to work-family conflict, work-family positive spillover, job satisfaction, and turnover intentions over and above measures of general supervisor support.     

A survey on nonadaptive group testing algorithms through the angle of decoding

Group testing, sometimes called pooling design, has been applied to a variety of problems such as blood testing, multiple access communication, coding theory, among others. Recently, screening experiments in molecular biology has become the most important application. In this paper, we review several models in this application by focusing on decoding, namely, giving a comparative study of how the problem is solved in each of these models.     

Tracing ‘driver’ versus ‘modulator’ information flow throughout large-scale,task-related neural circuitry

Primary objective: To determine the relative uses of neural action potential (‘spike’) data versus local field potentials (LFPs) for modeling information flow through complex brain networks. Hypothesis: The common use of LFP data, which are continuous and therefore more mathematically suited for spectral information-flow modeling techniques such as Granger causality analysis, can lead to spurious inferences about whether a given brain area ‘drives’ the spiking in a downstream area. Experiment: We recorded spikes and LFPs from the forelimb motor cortex (M1) and the magnocellular red nucleus (mRN), which receives axon collaterals from M1 projection cells onto its distal dendrites, but not onto its perisomatic regions, as rats performed a skilled reaching task. Results and implications: As predicted, Granger causality analysis on the LFPs—which are mainly composed of vector-summed dendritic currents—produced results that if conventionally interpreted would suggest that the M1 cells drove spike firing in the mRN, whereas analyses of spiking in the two recorded regions revealed no significant correlations. These results suggest that mathematical models of information flow should treat the sampled dendritic activity as more likely to reflect intrinsic dendritic and input-related processing in neural networks, whereas spikes are more likely to provide information about the output of neural network processing.     

On the inapproximability of the exemplar conserved interval distance problem of genomes

In this paper we present two main results about the inapproximability of the exemplar conserved interval distance problem of genomes. First, we prove that it is NP-complete to decide whether the exemplar conserved interval distance between any two genomes is zero or not. This result implies that the exemplar conserved interval distance problem does not admit any approximation in polynomial time, unless P=NP. In fact, this result holds, even when every gene appears in each of the given genomes at most three times. Second, we strengthen the first result under a weaker definition of approximation, called weak approximation. We show that the exemplar conserved interval distance problem does not admit any weak approximation within a super-linear factor of , where m is the maximal length of the given genomes. We also investigate polynomial time algorithms for solving the exemplar conserved interval distance problem when certain constrains are given. We prove that the zero exemplar conserved interval distance problem of two genomes is decidable in polynomial time when one genome is O(log n)-spanned. We also prove that one can solve the constant-sized exemplar conserved interval distance problem in polynomial time, provided that one genome is trivial.     

Hypothesis group testing for disjoint pairs

Classical group testing (CGT) is a widely applicable biotechnical technique used to identify a small number of distinguished objects from a population when the presence of any one of these distinguished objects among a group of others produces an observable result. This paper discusses a variant of CGT called group testing for disjoint pairs (GTAP). The difference between the two is that in GTDP, the distinguished items are pairs from, not individual objects in, the population. There are several biological examples of when this abstract model applies. One biological example is DNA hybridization. The presence of pairs of hybridized DNA strands can be detected in a pool of DNA strands. Another situation is the detection of binding interactions between prey and bait proteins. This paper gives a random pooling method, similar in spirit to hypothesis testing, which identifies pairs of objects from a population that collectively have an observable function. This method is simply to apply, achieves good results, is amenable to automation and can be easily modified to compensate for testing errors. M.A. Bishop is supported by AFOSR FA8750-06-C-0007. A.J. Macula is supported by NSF-0436298, AFOSR FA8750-06-C-0007.     

A reactive tabu search algorithm for the vehicle routing problem with simultaneous pickups and deliveries

The vehicle routing problem with pickups and deliveries (VRPPD) extends the vehicle routing problem (VRP) by allowing customers to both send and receive goods. The main difficulty of the problem is that the load of vehicles is fluctuating rather than decreasing as in the VRP. We design a reactive tabu search metaheuristic that can check feasibility of proposed moves quickly and reacts to repetitions to guide the search. Several new best solutions are found for benchmark problems.     

Central State Steering of Local Collaboration: Assessing the Impact of Tools of Meta-governance in Homelessness Services in England

Much of the discussion of state steering of service delivery networks to encourage collaboration at the local level has been theoretical. This study builds on this analysis systematically to assess the relationship between meta-governance tools of central government steering and the extent of local collaboration, using the case of homelessness services in England. Contrary to the pessimist expectations of some contemporary theory, central government funding tools and facilitating the transfer of best practice encourages collaboration. However, simple information provision and authority based tools are only partially effective, risking tokenistic compliance. Authority tools are more effective when combined with other tools.

Accountability in the Shadow of Hierarchy: The Horizontal Accountability of Agencies

In the Netherlands, new accountability arrangements are created as remedies for the accountability deficit of agencies. These arrangements are of a ‘horizontal’ nature, as the agencies account for their behaviour towards accountees that are not their hierarchical superiors: clients, stakeholders or peers. This paper provides an inventory and qualitative assessment of horizontal accountability arrangements. It is argued that they have added value because they stimulate learning. However, horizontal accountability is not a substitute for hierarchical accountability. Horizontal accountability arrangements operate ‘in the shadow of hierarchy’: they gain significance through complex interactions with traditional forms of accountability.

Citizen Support for Hospital Privatization: A Hierarchical Cross-National Analysis

The movement for a new public management (NPM) holds that citizens will put aside partisan and ideological differences to support more efficient service delivery. Focusing on hospital privatization, a multi-national sample is utilized to investigate this assertion. Generalized hierarchical linear modeling (GHLM) is employed as the method of statistical analysis. The findings with regard to privatizing hospitals reveal that citizens develop their policy position by reference to a combination of symbolic political variables, service demand, self-interest, information levels, and political efficacy. The results cast doubt on the assertion that partisan differences will be set aside as citizens consider privatization reforms.