Sensitivity Analysis Using Risk Measures

In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.     

Power and dependence perspectives on outsourcing decisions

Since the 1980s outsourcing has been a major topic in both economic and management literature. Economic literature tends to focus on the transaction cost perspective. In management literature the core competence approach prevails. An emerging alternative view on outsourcing is the power and dependence perspective. Aspects of power and dependence become visible in four case studies involving the maintenance of capital assets. In each instance both the buyer and supplier sides of the dyad are examined. The paper concludes with managerial implications and research opportunities.     

The Local Power of the CADF and CIPS Panel Unit Root Tests

Very little is known about the local power of second generation panel unit root tests that are robust to cross-section dependence. This article derives the local asymptotic power functions of the cross-section argumented Dickey–Fuller Cross-section Augmented Dickey-Fuller (CADF) and CIPS tests of Pesaran (2007), which are among the most popular tests around.     

Kernel density estimation under negative superadditive dependence and its application for real data

In this paper, the kernel density estimator for negatively superadditive dependent random variables is studied. The exponential inequalities and the exponential rate for the kernel estimator of density function with a uniform version, over compact sets are investigated. Also, the optimal bandwidth rate of the estimator is obtained using mean integrated squared error. The results are generalized and used to improve the ones obtained for the case of associated sequences. As an application, FGM sequences that fulfil our assumptions are investigated. Also, the convergence rate of the kernel density estimator is illustrated via a simulation study. Moreover, a real data analysis is presented.     

Uniform consistency rate of kNN regression estimation for functional time series data

In this paper, we investigate the k-nearest neighbours (kNN) estimation of nonparametric regression model for strong mixing functional time series data. More precisely, we establish the uniform almost complete convergence rate of the kNN estimator under some mild conditions. Furthermore, a simulation study and an empirical application to the real data analysis of sea surface temperature (SST) are carried out to illustrate the finite sample performances and the usefulness of the kNN approach.     

Exploring the link between government funding and efficiency in nonprofit colleges

Previous literature has suggested that federal funding can hinder the efficiency of nonprofit organizations, but this has yet not been empirically tested. This study used a two‐stage data envelopment analysis (DEA) model to measure the efficiency of a set of private nonprofit teaching‐oriented colleges, then estimate the impact of federal and state funding on organizational efficiency. The findings indicate that, on average, increases in neither state nor federal funding effect efficiency of nonprofit colleges. Increasing state funding negatively impacted the efficiency of public colleges (N = 799). This study suggests that the challenges for nonprofits that accompany government funding may not rise to a quantifiable negative effect on efficiency.     

Regression models to dependence for exceedance

Extreme Value Theory (EVT) aims to study the tails of probability distributions in order to measure and quantify extreme events of maximum and minimum. In river flow data, an extreme level of a river may be related to the level of a neighboring river that flows into it. In this type of data, it is very common for flooding of a location to have been caused by a very large flow from an affluent river that is tens or hundreds of kilometers from this location. In this sense, an interesting approach is to consider a conditional model for the estimation of a multivariate model. Inspired by this idea, we propose a Bayesian model to describe the dependence of exceedance between rivers, where we considered a conditionally independent structure. In this model, the dependence between rivers is captured by modeling the excess marginally of one river as a consequence of linear functions of the other rivers. The results showed that there is a strong and positive connection between excesses in one river caused by the excesses of the other rivers.     

Generalized Augmentation to Control the False Discovery Exceedance in Multiple Testing

Abstract.  A new multiple testing procedure, the generalized augmentation procedure (GAUGE), is introduced. The procedure is shown to control the false discovery exceedance and to be competitive in terms of power. It is also shown how to apply the idea of GAUGE to achieve control of other error measures. Extensions to dependence are discussed, together with a modification valid under arbitrary dependence. We present an application to an original study on prostate cancer and on a benchmark data set on colon cancer.     

Complex home care: challenges arising from the blurring of boundaries between family and professional care

Over the last 50 years, increasingly complex care (such as tracheostomy management, dialysis or enteral feeding) has shifted from hospital to home, with a concomitant rise in patient self-management and care given by family members. Recognition of the importance of the contribution of family care to the health system is also growing. This article reports the findings of a New Zealand study which explored the experiences of family carers who manage technical health procedures at home. It then draws attention to some broader issues raised by shifting complex care from professional management in hospital settings to family care at home, namely the ways in which complex home care blurs the boundaries between professional and family care (creating the ambiguous position of the expert carer) and questions of safety and responsibility in family care. We also discuss the implications for policy around family caregiving in New Zealand. Given the potential physical and mental health impacts of caring, it is time for renewed consideration of what family carers should be expected or allowed to manage and how the health system can support them in their important role.     

Continuity of f-projections and applications to the iterative proportional fitting procedure*

This paper proves continuity of f-projections and the continuous dependence of the limit matrix of the iterative proportional fitting procedure (IPF procedure) on the given matrix as well as the given marginals under certain regularity constraints. For finite spaces, the concept of f-projections of finite measures on a compact and convex set is introduced and continuity of f-projections is proven. This result is applied to the IPF procedure. Given a nonnegative matrix as well as row and column marginals the IPF procedure generates a sequence of matrices, called the IPF sequence, by alternately fitting rows and columns to match their respective marginals. The procedure is equivalent to cyclic f-projections. If the IPF sequence converges, the application of the continuity of f-projections yields the continuous dependence of the limit matrix on the given matrix. By generalized convex programming and under some constraints, it is shown that the limit matrix of the IPF sequence continuously depends not only on the given matrix but also on the marginals.