管理者关系对企业资源获取的影响:一种结构性观点

资源获取是企业利用管理者的社会关系提升绩效的重要机制之一。基于资源依赖理论,对管理者关系、环境包容性与环境不确定性对企业资源获取的结构性影响的检验结果表明:管理者关系有利于企业从外部获取资源;环境不确定性加强管理者关系对资源获取的影响;环境包容性削弱管理者关系对资源获取的影响;强管理者关系、高环境不确定性与低环境包容性的结构配置能更好地帮助企业获取外部资源。     

Using copulas for rating weather index insurance contracts

This study develops a methodology for a copula-based weather index insurance design. Because the copula approach is better suited for modeling tail dependence than the standard linear correlation approach, its use may increase the effectiveness of weather insurance contracts designed to provide protection against extreme weather events. In our study, we employ three selected Archimedean copulas to capture the left-tail dependence in the joint distribution of the farm yield and a specific weather index. A hierarchical Bayesian model is applied to obtain consistent estimates of tail dependence using relatively short time series. Our empirical results for 47 large grain-producing farms from Kazakhstan indicate that, given the choice of an appropriate weather index to signal catastrophic events, such as a severe drought, copula-based weather insurance contracts may provide significantly higher risk reductions than regression-based indemnification schemes.     

Asymptotic normality of the local linear estimation of the conditional density for functional time-series data

This article focuses on the conditional density of a scalar response variable given a random variable taking values in a semimetric space. The local linear estimators of the conditional density and its derivative are considered. It is assumed that the observations form a stationary α-mixing sequence. Under some regularity conditions, the joint asymptotic normality of the estimators of the conditional density and its derivative is established. The result confirms the prospect in Rachdi et al. (2014 Rachdi, M., A. Laksaci, J. Demongeot, A. Abdali, and F. Madani. 2014. Theoretical and practical aspects of the quadratic error in the local linear estimation of the conditional density for functional data. Computational Statistics and Data Analysis 73 :53–68.[Crossref], [Web of Science ®] , [Google Scholar]) and can be applied in time-series analysis to make predictions and build confidence intervals. The finite-sample behavior of the estimator is investigated by simulations as well.     

Computing the conditional stationary distribution in Markov chains of level-dependent M/G/1-type

This paper considers the computation of the conditional stationary distribution in Markov chains of level-dependent M/G/1-type, given that the level is not greater than a predefined threshold. This problem has been studied recently and a computational algorithm is proposed under the assumption that matrices representing downward jumps are nonsingular. We first show that this assumption can be eliminated in a general setting of Markov chains of level-dependent G/G/1-type. Next we develop a computational algorithm for the conditional stationary distribution in Markov chains of level-dependent M/G/1-type, by modifying the above-mentioned algorithm slightly. In principle, our algorithm is applicable to any Markov chain of level-dependent M/G/1-type, if the Markov chain is irreducible and positive-recurrent. Furthermore, as an input to the algorithm, we can set an error bound for the computed conditional distribution, which is a notable feature of our algorithm. Some numerical examples are also provided.     

Nonparametric estimation of multivariate extreme-value copulas

Extreme-value copulas arise in the asymptotic theory for componentwise maxima of independent random samples. An extreme-value copula is determined by its Pickands dependence function, which is a function on the unit simplex subject to certain shape constraints that arise from an integral transform of an underlying measure called spectral measure. Multivariate extensions are provided of certain rank-based nonparametric estimators of the Pickands dependence function. The shape constraint that the estimator should itself be a Pickands dependence function is enforced by replacing an initial estimator by its best least-squares approximation in the set of Pickands dependence functions having a discrete spectral measure supported on a sufficiently fine grid. Weak convergence of the standardized estimators is demonstrated and the finite-sample performance of the estimators is investigated by means of a simulation experiment.     

Incapsulating hands: on the (in)dependence of young adults in the UNHCR’s Maratane Refugee Camp

Drawing on research with a small group of young women and men in Maratane Refugee Camp, Mozambique, this paper argues that youth envisage themselves as incapsulated in the camp’s physical and ideological boundaries. It shows that the United Nations’ (UN) mandate of finding durable solutions to international problems is difficult to achieve when young people envisage themselves as reliant or dependent on the UN. It argues that greater attention needs to be given to the mentoring of young people within the UN system, so that they are equipped with viable and realistic life expectations and skills.     

Bursts and heavy tails of execution time in business processes

This paper investigates the phenomena and effects of heavy tails of execution time in business processes. We approached the heavy tails as a particular variation of business process execution, and analysed them from empirical data of its execution time. The features and possible causes of the heavy tails were investigated. A workflow simulation was conducted to simulate the heavy tails, in order to evaluate quantitatively their effects to business process performance. The simulation outcomes demonstrate that (1) even a heavy-tailed activity in a business process can cause large variation of end-to-end execution times of the process and (2) there are significant prediction gaps resulted by different assumptions of execution times between an exponential distribution and a heavy-tailed distribution.     

Pitfalls in using Weibull tailed distributions

By assuming that the underlying distribution belongs to the domain of attraction of an extreme value distribution, one can extrapolate the data to a far tail region so that a rare event can be predicted. However, when the distribution is in the domain of attraction of a Gumbel distribution, the extrapolation is quite limited generally in comparison with a heavy tailed distribution. In view of this drawback, a Weibull tailed distribution has been studied recently. Some methods for choosing the sample fraction in estimating the Weibull tail coefficient and some bias reduction estimators have been proposed in the literature. In this paper, we show that the theoretical optimal sample fraction does not exist and a bias reduction estimator does not always produce a smaller mean squared error than a biased estimator. These are different from using a heavy tailed distribution. Further we propose a refined class of Weibull tailed distributions which are more useful in estimating high quantiles and extreme tail probabilities.