Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

The covariance of the backward and forward recurrence times in a renewal process: the stationary case and asymptotics for the ordinary case

In a Poisson process, it is well-known that the forward and backward recurrence times at a given time point t are independent random variables. In a renewal process, although the joint distribution of these quantities is known (asymptotically), it seems that very few results regarding their covariance function exist. In the present paper, we study this covariance and, in particular, we state both necessary and sufficient conditions for it to be positive, zero or negative in terms of reliability classifications and the coefficient of variation of the underlying inter-renewal and the associated equilibrium distribution. Our results apply either for an ordinary renewal process in the steady state or for a stationary process.     

A volume based approach to establish B-spline based expressions for density functions and its application to progressive hybrid censoring

We propose an approach to determine the distribution of particular linear combinations of hybrid censored order statistics which is based on the calculation of volumes of polytopes. For this purpose, we establish efficient and compact volume formulas in terms of B-splines. Further, we illustrate our approach for ten different progressive hybrid censoring schemes under an exponential assumption.     

Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions

We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr.     

商业信用风险管理绩效影响因素的实证分析

近年来,Jianmin Jia等学者对"我国信用风险管理现状"及其相关问题进行研究,取得一定的研究成果,但相应研究中存在着"简单使用线性回归,未能消除变量间的相互影响"等局限。基于对已有相关研究成果的评析,并运用因子分析和Pearson相关分析方法,对我国商业信用风险管理绩效的影响因素进行实证研究。研究表明:商业信用风险管理绩效的关键影响因素为"信用管理职能设置"、"客户资信评估"和"信用政策制定",我国企业应从"完善信用风险管理职能"、"改善信息不对称状况"和"提高信用政策制定水平"三方面提高风险管理绩效。     

基于共同持有资产的银行间接关联网络研究

金融市场是一个复杂系统,银行之间不仅有直接的拆借关系,还能够通过投资市场建立间接关联。本文分析了资产内生相关性、资产降价出售以及银行投资行为等因素对银行间接关联程度的影响,基于这些因素通过持有共同资产构建银行系统的间接关联,并以此为基础通过平面极大过滤图方法生成银行间接关联网络,论证了该网络中的系统重要性银行,并通过对该网络结构特征的分析,发现间接关联网络具有小世界和无标度等特征,这些特征与银行通过直接拆借形成的网络相似。本文对于银行间接关联网络的研究不仅能够更加清晰地了解银行之间的关系,也为银行系统性风险的监管提供了新的视角。     

医学教育中隐性课程的相关性研究

近年来,隐性课程的研究成为一个热点问题,特别是在高等教育领域备受关注。从教育界对隐性课程的研究角度（隐性课程的概念、特征、功能、建设与管理几个方面）切入,对实践中存在的一些问题进行分析,以期对以后的研究有所帮助。     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.