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.     

基于理想解和灰关联度的动态评价方法及其应用研究

为了对多个多属性（指标）待评价对象（方案）在多个时间点的发展状态和该时间段内的总体发展水平进行比较分析，根据理想解法和灰关联度法优缺点，提出基于理想解和灰关联度的动态评价方法。该方法基于三维数据，将欧氏距离和灰色关联度相结合，提出一种新贴近度，同时反映了位置关系和数据曲线的相似性差异，兼顾评价指标值差异程度和增长程度。最后将该方法应用于"十二五"期间省域循环经济生态效益评价，通过实例验证该方法实际应用上的有效性。     

区域一体化背景下城市土地利用效率的空间关联及其解释——以长江中游城市群为例

从理论层面揭示区域一体化影响城市土地利用效率（ULUE）的内在机理，构建区域一体化背景下“规模+结构+集聚”的ULUE测度体系，并综合利用小波神经网络、空间分异模型和空间回归分析探索2003-2015年长江中游城市群ULUE的空间关联特征及其影响因素。结果发现：①区域一体化背景下长江中游城市群ULUE从2003的0.561 4增长至2015年的0.811 1，且呈现出明显的区域差异。②ULUE并非随机分布，而是具有空间上的关联特征和集群趋势。且由于不同集聚区内扩散效应在空间上渗透不均衡，其关联性主要表现为空间依赖性和空间异质性。③区域一体化背景下长江中游城市群ULUE在空间关联上具有“近水楼台先得月”和“门当户对”的特征，地理位置的空间相邻、投资消费结构和产业结构的相似是其空间关联的主要成因。     

基于信号博弈的卖方欺诈行为分析

本文主要基于信号博弈的卖方欺诈行为进行研究，假设拍卖中可能存在欺诈型和诚实型两种卖家，其中欺诈行为有概率发生在第二价格拍卖中：欺诈型卖方冒充竞拍者递交仅次于最高价的报价从而获得额外收益。两种卖家根据各自效用选择拍卖形式：第一价格或者第二价格。而竞买者将卖者的选择作为信号，更新对卖方类型的判断，然后制定报价策略。这是一个买卖方信号交叉影响的过程。考虑到拍卖过程中买方价值相关性，本文在建立模型中参考了关联价值原理。针对该模型进行分析，得出了不同情况下的买卖方策略，并且研究了买方报价、买方判断、卖方收益三者之间的关系。文中利用贝叶斯公式对双方的策略选择问题进行预测，与单纯的概率分布方法相比，更具实践价值。     

The effects of data input errors on subsequent statistical inference

Data input errors can potentially affect statistical inferences, but little research has been published to date on this topic. In the present paper, we report the effect of data input errors on the statistical inferences drawn about population parameters in an empirical study involving 280 students from two Polish universities, namely the Warsaw University of Life Sciences – SGGW and the University of Information Technology and Management in Rzeszow. We found that 28% of the students committed at least one data error. While some of these errors were small and did not have any real effect, a few of them had substantial effects on the statistical inferences drawn about the population parameters.     

An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine

The article focuses on the application of the Bayesian networks (BN) technique to problems of personalized medicine. The simple (intuitive) algorithm of BN optimization with respect to the number of nodes using naive network topology is developed. This algorithm allows to increase the BN prediction quality and to identify the most important variables of the network. The parallel program implementing the algorithm has demonstrated good scalability with an increase in the computational cores number, and it can be applied to the large patients database containing thousands of variables. This program is applied for the prediction for the unfavorable outcome of coronary artery disease (CAD) for patients who survived the acute coronary syndrome (ACS). As a result, the quality of the predictions of the investigated networks was significantly improved and the most important risk factors were detected. The significance of the tumor necrosis factor-alpha gene polymorphism for the prediction of the unfavorable outcome of CAD for patients survived after ACS was revealed for the first time.     

Efficient Inference for Longitudinal Data Varying‐coefficient Regression Models

Informative identification of the within‐subject correlation is essential in longitudinal studies in order to forecast the trajectory of each subject and improve the validity of inferences. In this paper, we fit this correlation structure by employing a time adaptive autoregressive error process. Such a process can automatically accommodate irregular and possibly subject‐specific observations. Based on the fitted correlation structure, we propose an efficient two‐stage estimator of the unknown coefficient functions by using a local polynomial approximation. This procedure does not involve within‐subject covariance matrices and hence circumvents the instability of calculating their inverses. The asymptotic normality of resulting estimators is established. Numerical experiments were conducted to check the finite sample performance of our method and an example of an application involving a set of medical data is also illustrated.     

Correlation properties of continuous-time autoregressive processes delayed by the inverse of the stable subordinator

Abstract

We define the delayed Lévy-driven continuous-time autoregressive process via the inverse of the stable subordinator. We derive correlation structure for the observed non-stationary delayed Lévy-driven continuous-time autoregressive processes of order p, emphasizing low orders, and we show they exhibit long-range dependence property. Distributional properties are discussed as well.     

Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data

Graphical models capture the conditional independence structure among random variables via existence of edges among vertices. One way of inferring a graph is to identify zero partial correlation coefficients, which is an effective way of finding conditional independence under a multivariate Gaussian setting. For more general settings, we propose kernel partial correlation which extends partial correlation with a combination of two kernel methods. First, a nonparametric function estimation is employed to remove effects from other variables, and then the dependence between remaining random components is assessed through a nonparametric association measure. The proposed approach is not only flexible but also robust under high levels of noise owing to the robustness of the nonparametric approaches.     

Asymptotic distribution of regression correlation coefficient for Poisson regression model

This study treats an asymptotic distribution for measures of predictive power for generalized linear models (GLMs). We focus on the regression correlation coefficient (RCC) that is one of the measures of predictive power. The RCC, proposed by Zheng and Agresti is a population value and a generalization of the population value for the coefficient of determination. Therefore, the RCC is easy to interpret and familiar. Recently, Takahashi and Kurosawa provided an explicit form of the RCC and proposed a new RCC estimator for a Poisson regression model. They also showed the validity of the new estimator compared with other estimators. This study discusses the new statistical properties of the RCC for the Poisson regression model. Furthermore, we show an asymptotic normality of the RCC estimator.