Spatial Matérn Fields Driven by Non‐Gaussian Noise

The article studies non‐Gaussian extensions of a recently discovered link between certain Gaussian random fields, expressed as solutions to stochastic partial differential equations (SPDEs), and Gaussian Markov random fields. The focus is on non‐Gaussian random fields with Matérn covariance functions, and in particular, we show how the SPDE formulation of a Laplace moving‐average model can be used to obtain an efficient simulation method as well as an accurate parameter estimation technique for the model. This should be seen as a demonstration of how these techniques can be used, and generalizations to more general SPDEs are readily available.     

Stochastic approximation Monte Carlo EM for change-point analysis

In the expectation–maximization (EM) algorithm for maximum likelihood estimation from incomplete data, Markov chain Monte Carlo (MCMC) methods have been used in change-point inference for a long time when the expectation step is intractable. However, the conventional MCMC algorithms tend to get trapped in local mode in simulating from the posterior distribution of change points. To overcome this problem, in this paper we propose a stochastic approximation Monte Carlo version of EM (SAMCEM), which is a combination of adaptive Markov chain Monte Carlo and EM utilizing a maximum likelihood method. SAMCEM is compared with the stochastic approximation version of EM and reversible jump Markov chain Monte Carlo version of EM on simulated and real datasets. The numerical results indicate that SAMCEM can outperform among the three methods by producing much more accurate parameter estimates and the ability to achieve change-point positions and estimates simultaneously.     

基于“云排队”的经济效益分析

随着各种云智能排队预约系统的推出,顾客“排队难”的问题得到了极大的缓解。其简便的操作方式,受到了大量商家与顾客的好评。本文基于当下热门服务行业的运营模式,建立排队博弈模型,从理论上分析了“云排队”平台对顾客以及服务商的影响。本文主要得到了以下几个结果：(1) 给出了两类顾客的纳什均衡策略(包括进队策略与最优到达间隔);(2) 发现当前的预约机制能够提高“线上”顾客的效用,并刺激更多的线上顾客购买该产品,但同时也降低了“线下”顾客的进队意愿;(3) 发现在云排队机制下服务商的收益将大大提高,同时该机制将刺激服务商收取更高的服务费用。     

Empirical likelihood ratio with doubly truncated data

Doubly truncated data appear in a number of applications, including astronomy and survival analysis. For doubly-truncated data, the lifetime T is observable only when U≤T≤V, where U and V are the left-truncated and right-truncated time, respectively. Based on the empirical likelihood approach of Zhou [21 Zhou, M. 2005. Empirical likelihood ratio with arbitrarily censored/truncated data by EM algorithm. J. Comput. Graph. Statist., 14: 643–656. [Taylor & Francis Online], [Web of Science ®] [Google Scholar]], we propose a modified EM algorithm of Turnbull [19 Turnbull, B. W. 1976. The empirical distribution function with arbitrarily grouped censored and truncated data. J. R. Stat. Soc. Ser. B, 38: 290–295.  [Google Scholar]] to construct the interval estimator of the distribution function of T. Simulation results indicate that the empirical likelihood method can be more efficient than the bootstrap method.     

校园外道路交通事故人身损害赔偿案理论与实务问题研究--关于未成年人人身损害赔偿项目的探讨

校园外道路交通事故人身损害赔偿案在司法实践中存在着一些理论与实务问题。本文通过研究南京市鼓楼区人民法院少年庭的相关案例,分析此类案件受案率呈上升趋势,但调解结案率低,赔偿内容不规范,进而对涉及未成年人特点的赔偿项目进行了理论分析与探讨。     

Expectation maximization estimates of the offspring probabilities in a class of multitype branching processes with binary family trees

When proliferating cells are counted in several independent colonies at some time points, the maximum likelihood estimates of the parameters of the multitype branching process are obtained trough an expectation maximization algorithm. In the case of an offspring distribution governed by a Markov branching process with binary family trees, this method, relying then on a partial knowledge of the tree, yields the same estimates as those computed with the complete knowledge of the tree.     

Robust Estimation for Zero‐Inflated Poisson Regression

Abstract. The zero‐inflated Poisson regression model is a special case of finite mixture models that is useful for count data containing many zeros. Typically, maximum likelihood (ML) estimation is used for fitting such models. However, it is well known that the ML estimator is highly sensitive to the presence of outliers and can become unstable when mixture components are poorly separated. In this paper, we propose an alternative robust estimation approach, robust expectation‐solution (RES) estimation. We compare the RES approach with an existing robust approach, minimum Hellinger distance (MHD) estimation. Simulation results indicate that both methods improve on ML when outliers are present and/or when the mixture components are poorly separated. However, the RES approach is more efficient in all the scenarios we considered. In addition, the RES method is shown to yield consistent and asymptotically normal estimators and, in contrast to MHD, can be applied quite generally.     

Inference methods for saturated models in longitudinal clinical trials with incomplete binary data

In the longitudinal studies with binary response, it is often of interest to estimate the percentage of positive responses at each time point and the percentage of having at least one positive response by each time point. When missing data exist, the conventional method based on observed percentages could result in erroneous estimates. This study demonstrates two methods of using expectation-maximization (EM) and data augmentation (DA) algorithms in the estimation of the marginal and cumulative probabilities for incomplete longitudinal binary response data. Both methods provide unbiased estimates when the missingness mechanism is missing at random (MAR) assumption. Sensitivity analyses have been performed for cases when the MAR assumption is in question.     

交通方式选择与旅客时间价值研究--以北京-太原客运通道为例

从旅客出行交通方式选择入手,在综合考虑旅客收入、出行费用、出行时间、安全舒适度、服务水平等因素基础上,提出一个简化且合理的时间价值计算模型。以北京-太原客运通道为例,通过对不同收入旅客出行交通方式选择进行调查,计算出不同收入阶层旅客的时间价值。结果发现,不同收入的旅客时间价值是不同的,但不同收入旅客的每小时时间价值约等于该群体每小时人均可支配收入的50%左右。这一结论对各种交通运输工具的定价及交通资源的配置具有一定的借鉴意义。     

公司价值解析

作者认为,相对于其重要性而言,关于公司价值尤其是其定量方面的研究很不充分。作者在总结前人的研究,建立了公司价值的定量描述模型,分析了公司价值增长的类型,说明了内部融资和外部融资下公司价值的不同,进而解释了上市公司普遍存在的“规模偏好”和“圈钱偏好”。